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An Objective Structured Practical Examination (OSPE) is an effective and robust, but resource-intensive, means of evaluating anatomical knowledge. Since most OSPEs employ short answer or fill-in-the-blank style questions, the format requires many people familiar with the content to mark the examinations. However, the increasing prevalence of online delivery for anatomy and physiology courses could result in students losing the OSPE practice that they would receive in face-to-face learning sessions. The purpose of this study was to test the accuracy of Decision Trees (DTs) in marking OSPE questions as a first step to creating an intelligent, online OSPE tutoring system. The study used the results of the winter 2020 semester final OSPE from McMaster University's anatomy and physiology course in the Faculty of Health Sciences (HTHSCI 2FF3/2LL3/1D06) as the data set. Ninety percent of the data set was used in a 10-fold validation algorithm to train a DT for each of the 54 questions. Each DT was comprised of unique words that appeared in correct, student-written answers. The remaining 10% of the data set was marked by the generated DTs. When the answers marked by the DT were compared to the answers marked by staff and faculty, the DT achieved an average accuracy of 94.49% across all 54 questions. This suggests that machine learning algorithms such as DTs are a highly effective option for OSPE grading and are suitable for the development of an intelligent, online OSPE tutoring system., (© 2023 The Authors. Anatomical Sciences Education published by Wiley Periodicals LLC on behalf of American Association for Anatomy.)

Introduction: There are no validated decision-making algorithms concerning severe asthma (SA) management. Future risks are crucial factors and can be derived from SA trajectories., Areas Covered: The future severe asthma-decision trees should revisit current knowledge and gaps. A focused literature search has been conducted., Expert Opinion: Asthma severity is currently defined a priori , thereby precluding a role for early interventions aiming to prevent outcomes such as exacerbations (systemic corticosteroids exposure) and lung function decline. Asthma 'at-risk' might represent the ultimate paradigm but merits longitudinal studies considering modern interventions. Real exacerbations, severe airway hyperresponsiveness, excessive T2-related biomarkers, noxious environments and patient behaviors, harms of OCS and high-doses inhaled corticosteroids (ICS), and low adherence-to-effectiveness ratios of ICS-containing inhalers are predictors of future risks. New tools such as imaging, genetic, and epigenetic signatures should be used. Logical and numerical artificial intelligence may be used to generate a consistent risk score. A pragmatic definition of response to treatments will allow development of a validated and applicable algorithm. Biologics have the best potential to minimize the risks, but cost remains an issue. We propose a simplified six-step algorithm for decision-making that is ultimately aiming to achieve asthma remission.

Pyrolysis is an efficient thermochemical conversion process, but accurate prediction of yield and properties of biochar presents a significant challenge. Three prominent ensemble learning methods, viz. Random Forest (RF), eXtreme Gradient Boosting (XGB), and Adaptive Boosting (AdaBoost) were utilized to develop models to predict yield and higher heating value (HHV) of biochar. Dataset comprising 423 observations from 44 different biomasses was curated from peer-reviewed journals for predicting biochar yield. RF regressor achieved a test R2 of 0.86 for biochar yield, while XGB regressor achieved a test R2 of 0.87 for biochar HHV prediction. The SHapley Additive exPlanations (SHAP) analysis was conducted to assess influence of each feature on the model's output. Pyrolysis temperature and ash content of biomass were identified as the most influential features for the prediction of both yield and HHV of biochar. The partial dependence plots (PDPs) revealed nonlinear relationships, interpreting how the model formulates its predictions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

PPG (photoplethysmography) holds significant application value in wearable and intelligent health devices. However, during the acquisition process, PPG signals can generate motion artifacts due to inevitable coupling motion, which diminishes signal quality. In response to the challenge of real-time detection of motion artifacts in PPG signals, this study analyzed the generation and significant features of PPG signal interference. Seven features were extracted from the pulse interval data, and those exhibiting notable changes were filtered using the dual-sample Kolmogorov-Smirnov test. The real-time detection of motion artifacts in PPG signals was ultimately based on decision trees. In the experimental phase, PPG signal data from 20 college students were collected to formulate the experimental dataset. The experimental results demonstrate that the proposed method achieves an average accuracy of (94.07±1.14)%, outperforming commonly used motion artifact detection algorithms in terms of accuracy and real-time performance.

Animal psychophysics can generate rich behavioral datasets, often comprised of many 1000s of trials for an individual subject. Gradient-boosted models are a promising machine learning approach for analyzing such data, partly due to the tools that allow users to gain insight into how the model makes predictions. We trained ferrets to report a target word's presence, timing, and lateralization within a stream of consecutively presented non-target words. To assess the animals' ability to generalize across pitch, we manipulated the fundamental frequency (F0) of the speech stimuli across trials, and to assess the contribution of pitch to streaming, we roved the F0 from word token to token. We then implemented gradient-boosted regression and decision trees on the trial outcome and reaction time data to understand the behavioral factors behind the ferrets' decision-making. We visualized model contributions by implementing SHAPs feature importance and partial dependency plots. While ferrets could accurately perform the task across all pitch-shifted conditions, our models reveal subtle effects of shifting F0 on performance, with within-trial pitch shifting elevating false alarms and extending reaction times. Our models identified a subset of non-target words that animals commonly false alarmed to. Follow-up analysis demonstrated that the spectrotemporal similarity of target and non-target words rather than similarity in duration or amplitude waveform was the strongest predictor of the likelihood of false alarming. Finally, we compared the results with those obtained with traditional mixed effects models, revealing equivalent or better performance for the gradient-boosted models over these approaches., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Griffiths et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Nurses in neurointensive care units (NCUs) commonly use physical restraint (PR) to prevent adverse events like unplanned removal of devices (URDs) or falls. However, PR use should be based on evidenced decisions as it has drawbacks. Unfortunately, there is a lack of research-based PR protocol to support decision-making for nurses, especially for neurocritical patients., Aim: This study developed a restraint decision tree for neurocritical patients (RDT-N) to assist nurses in making PR decisions. We assessed its effectiveness in reducing PR use and adverse events., Study Design: This study employed a baseline and post-intervention test design at a NCU with 19 beds and 45 nurses in a tertiary hospital in a metropolitan city in South Korea. Two-hundred and thirty-seven adult patients were admitted during the study period. During the intervention, nurses were trained on the RDT-N. PR use and adverse events between the baseline and post-intervention periods were compared., Results: Post-intervention, total number of restrained patients decreased (20.7%-16.3%; χ 2  = 7.68, p = .006), and the average number of PR applied per restrained patient decreased (2.42-1.71; t = 5.74, p < .001). The most frequently used PR type changed from extremity cuff to mitten (χ 2  = 397.62, p < .001). No falls occurred during the study periods. On the other hand, URDs at baseline were 18.67 cases per 1000 patient days in the high-risk group and 5.78 cases per 1000 patient days in the moderate-risk group; however, no URD cases were reported post-intervention., Conclusions: The RDT-N effectively reduced PR use and adverse events. Its application can enhance patient-centred care based on individual condition and potential risks in NCUs., Relevance to Clinical Practice: Nurses can use the RDT-N to assess the need for PR in caring for neurocritical patients, reducing PR use and adverse events., (© 2024 British Association of Critical Care Nurses.)

This study aimed to investigate the factors associated with suicidal ideation in schizophrenia patients in China using decision tree and logistic regression models. From October 2020 to March 2022, patients with schizophrenia were chosen from Chifeng Anding Hospital and Daqing Third Hospital in Heilongjiang Province. A total of 300 patients with schizophrenia who met the inclusion criteria were investigated by questionnaire. The questionnaire covered general data, suicidal ideation, childhood trauma, social support, depressive symptoms and psychological resilience. Logistic regression analysis revealed that childhood trauma and depressive symptoms were risk factors for suicidal ideation in schizophrenia ( OR  = 2.330, 95% CI : 1.177 ~ 4.614; OR  = 10.619, 95% CI : 5.199 ~ 21.688), while psychological resilience was a protective factor for suicidal ideation in schizophrenia ( OR  = 0.173, 95% CI : 0.073 ~ 0.409). The results of the decision tree model analysis demonstrated that depressive symptoms, psychological resilience and childhood trauma were influential factors for suicidal ideation in patients with schizophrenia ( p  < 0.05). The area under the ROC for the logistic regression model and the decision tree model were 0.868 ( 95% CI : 0.821 ~ 0.916) and 0.863 ( 95% CI : 0.814 ~ 0.912) respectively, indicating excellent accuracy of the models. Meanwhile, the logistic regression model had a sensitivity of 0.834 and a specificity of 0.743 when the Youden index was at its maximum. The decision tree model had a sensitivity of 0.768 and a specificity of 0.8. Decision trees in combination with logistic regression models are of high value in the study of factors influencing suicidal ideation in schizophrenia patients.

Background: Temporomandibular disorders (TMD) do not only occur in adults but also in adolescents, with negative impacts on their development., Aim: To propose a predictive model for TMD in adolescents using a decision tree (DT) analysis and to identify groups at high and low risk of developing TMD in the city of Recife, PE, Brazil., Design: This cross-sectional study was conducted in Recife on 1342 schoolchildren of both sexes aged 10-17 years. The analyses were performed using Pearson's chi-squared test and Fisher's exact test, as well as the CHAID algorithm for the construction of the DT. The SPSS statistical program was used., Results: The prevalence of TMD was 33.2%. Statistically significant associations were observed between TMD and sex, depression, self-reported orofacial pain, and orofacial pain on clinical examination. The DT consisted of self-reported orofacial pain, orofacial pain on physical examination, and depression, with an overall predictive power of 73.0%., Conclusion: The proposed tree has a good predictive capacity and permits to identify groups at high risk of developing TMD among adolescents, such as those with self-reported orofacial pain or orofacial pain on examination associated with depression., (© 2023 BSPD, IAPD and John Wiley & Sons Ltd.)

The present study aimed to determine the prevalence of localized gingival enlargements (LGEs) and their clinical characteristics in a group of Thai patients, as well as utilize this information to develop a clinical diagnostic guide for predicting malignant LGEs. All LGE cases were retrospectively reviewed during a 20-year period. Clinical diagnoses, pathological diagnoses, patient demographic data, and clinical information were analyzed. The prevalence of LGEs was determined and categorized based on their nature, and concordance rates between clinical and pathological diagnoses among the groups were evaluated. Finally, a diagnostic guide was developed using clinical information through a decision tree model. Of 14,487 biopsied cases, 946 cases (6.53%) were identified as LGEs. The majority of LGEs were reactive lesions (72.62%), while a small subset was malignant tumors (7.51%). Diagnostic concordance rates were lower in malignant LGEs (54.93%) compared to non-malignant LGEs (80.69%). Size, consistency, color, duration, and patient age were identified as pivotal factors to formulate a clinical diagnostic guide for distinguishing between malignant and non-malignant LGEs. Using a decision tree model, we propose a novel diagnostic guide to assist clinicians in enhancing the accuracy of clinical differentiation between malignant and non-malignant LGEs., (© 2024. The Author(s).)

Childhood and adolescent overweight and obesity are one of the most serious public health challenges of the 21st century. A range of genetic, family, and environmental factors, and health behaviors are associated with childhood obesity. Developing models to predict childhood obesity requires careful examination of how these factors contribute to the emergence of childhood obesity. This paper has employed Multiple Linear Regression (MLR), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbour (KNN) models to predict the age at the onset of childhood obesity in Saudi Arabia (S.A.) and to identify the significant factors associated with it. De-identified data from Arar and Riyadh regions of S.A. were used to develop the prediction models and to compare their performance using multi-prediction accuracy measures. The average age at the onset of obesity is 10.8 years with no significant difference between boys and girls. The most common age group for onset is (5-15) years. RF model with the R2 = 0.98, the root mean square error = 0.44, and mean absolute error = 0.28 outperformed other models followed by MLR, DT, and KNN. The age at the onset of obesity was linked to several demographic, medical, and lifestyle factors including height and weight, parents' education level and income, consanguineous marriage, family history, autism, gestational age, nutrition in the first 6 months, birth weight, sleep hours, and lack of physical activities. The results can assist in reducing the childhood obesity epidemic in Saudi Arabia by identifying and managing high-risk individuals and providing better preventive care. Furthermore, the study findings can assist in predicting and preventing childhood obesity in other populations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Alanazi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Understanding and managing the health effects of Nitrogen Dioxide (NO 2 ) requires high resolution spatiotemporal exposure maps. Here, we developed a multi-stage multi-resolution ensemble model that predicts daily NO 2 concentration across continental France from 2005 to 2022. Innovations of this work include the computation of daily predictions at a 200 m resolution in large urban areas and the use of a spatio-temporal blocking procedure to avoid data leakage and ensure fair performance estimation. Predictions were obtained after three cascading stages of modeling: (1) predicting NO 2 total column density from Ozone Monitoring Instrument satellite; (2) predicting daily NO 2 concentrations at a 1 km spatial resolution using a large set of potential predictors such as predictions obtained from stage 1, land-cover and road traffic data; and (3) predicting residuals from stage 2 models at a 200 m resolution in large urban areas. The latter two stages used a generalized additive model to ensemble predictions of three decision-tree algorithms (random forest, extreme gradient boosting and categorical boosting). Cross-validated performances of our ensemble models were overall very good, with a ten-fold cross-validated R 2 for the 1 km model of 0.83, and of 0.69 for the 200 m model. All three basis learners participated in the ensemble predictions to various degrees depending on time and space. In sum, our multi-stage approach was able to predict daily NO 2 concentrations with a relatively low error. Ensembling the predictions maximizes the chance of obtaining accurate values if one basis learner fails in a specific area or at a particular time, by relying on the other learners. To the best of our knowledge, this is the first study aiming to predict NO 2 concentrations in France with such a high spatiotemporal resolution, large spatial extent, and long temporal coverage. Exposure estimates are available to investigate NO 2 health effects in epidemiological studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: This study aims to determine whether machine learning can identify specific combinations of long-term conditions (LTC) associated with increased sarcopenia risk and hence address an important evidence gap-people with multiple LTC (MLTC) have increased risk of sarcopenia but it has not yet been established whether this is driven by specific combinations of LTC., Design: Decision trees were used to identify combinations of LTC associated with increased sarcopenia risk. Participants were classified as being at risk of sarcopenia based on maximum grip strength of <32 kg for men and <19 kg for women. The combinations identified were triangulated with logistic regression., Setting: UK Biobank., Participants: UK Biobank participants with MLTC (two or more LTC) at baseline., Results: Of 140 001 participants with MLTC (55.3% women, median age 61 years), 21.0% were at risk of sarcopenia. Decision trees identified several LTC combinations associated with an increased risk of sarcopenia. These included drug/alcohol misuse and osteoarthritis, and connective tissue disease and osteoporosis in men, which showed the relative excess risk of interaction of 3.91 (95% CI 1.71 to 7.51) and 2.27 (95% CI 0.02 to 5.91), respectively, in age-adjusted models., Conclusion: Knowledge of LTC combinations associated with increased sarcopenia risk could aid the identification of individuals for targeted interventions, recruitment of participants to sarcopenia studies and contribute to the understanding of the aetiology of sarcopenia., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Objectives: Preoperative and intraoperative diagnostic tools influence the surgical management of primary hyperparathyroidism (PHPT), whereby their performance of classification varies considerably for the two common causes of PHPT: solitary adenomas and multiglandular disease. A consensus on the use of such diagnostic tools for optimal perioperative management of all PHPT patients has not been reached., Design: A decision tree model was constructed to estimate and compare the clinical outcomes and the cost-effectiveness of preoperative imaging modalities and intraoperative parathyroid hormone (ioPTH) monitoring criteria in a 21-year time horizon with a 3% discount rate. The robustness of the model was assessed by conducting a one-way sensitivity analysis and probabilistic uncertainty analysis., Setting: The US healthcare system., Population: A hypothetical population consisting of 5000 patients with sporadic, symptomatic or asymptomatic PHPT., Interventions: Preoperative and intraoperative diagnostic modalities for parathyroidectomy., Main Outcome Measures: Costs, quality-adjusted life-years (QALYs), net monetary benefits (NMBs) and clinical outcomes., Results: In the base-case analysis, four-dimensional (4D) CT was the least expensive strategy with US$10 276 and 15.333 QALYs. Ultrasound and 99m Tc-Sestamibi single-photon-emission CT/CT were both dominated strategies while 18 F-fluorocholine positron emission tomography was cost-effective with an NMB of US$416 considering a willingness to pay a threshold of US$95 958. The application of ioPTH monitoring with the Vienna criterion decreased the rate of reoperations from 10.50 to 0.58 per 1000 patients compared to not using ioPTH monitoring. Due to an increased rate of bilateral neck explorations from 257.45 to 347.45 per 1000 patients, it was not cost-effective., Conclusions: 4D-CT is the most cost-effective modality for the preoperative localisation of solitary parathyroid adenomas and multiglandular disease. The use of ioPTH monitoring is not cost-effective, but to minimise clinical complications, the Miami criterion should be applied for suspected solitary adenomas and the Vienna criterion for multiglandular disease., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Palatal groove represents a relatively uncommon developmental root anomaly, usually found on the palatal aspect of maxillary incisors. While its origin is controversial, its presence predisposes to severe periodontal defects., Aim: This study aimed to provide a systematic review of the literature focusing on the varied diagnostic techniques and treatment modalities for periodontal lesions arising from the presence of palatal groove. Based on the existing evidence and knowledge, the study also provides a comprehensive decisional tree, guiding clinicians in the challenging decision-making process face to a palatal groove., Methods: The literature search was conducted on Medline and Cochrane databases by two independent reviewers, who also performed the screening and selection process, looking for English written articles reporting on diagnosis and management (all treatment approaches) of periodontal lesion(s) associated with a palatal groove. Based on this literature, a comprehensive decisional tree, including a standardized palatal groove evaluation and tailored treatment approaches, is proposed. Moreover, a clinical case is described to demonstrate the practical application of the developed decisional tree., Results: Over a total of 451 articles initially identified, 34 were selected, describing 40 patients with 40 periodontal lesions associated with palatal grooves. The case report illustrates a deep, large, circumferential intra-bony defect on the palatal side of the tooth #22 associated with a shallow, moderately long palatal groove in an 18-year-old male patient. Following reevaluation, a single flap surgery was deemed necessary, combined with a regenerative procedure. At 2 years post-treatment, the tooth #22 is healthy, in a functional and esthetic position. The decision-making process, based on local and systemic patient's conditions, should allow an early and precise diagnosis to prevent further complications and undertake an adequate treatment., Conclusion: Palatal grooves are relatively rare; however, they are frequently associated with severe periodontal defects. The identification, diagnosis, prompt, and tailored management of the associated lesion is essential to mitigate potential periodontal and endodontic complications related to the presence of palatal groove., Systematic Review Registration: [ https://www.crd.york.ac.uk/prospero/ ], identifier [C CRD42022363194]., (© 2024. The Author(s).)

Technology plays a role in nearly every aspect of healthcare delivery. Health systems must continually invest in new and existing technology and analytics platforms to scale initiatives, enable innovation, and achieve interoperability to meet the needs and expectations of patients and clinicians while remaining focused on the organization's mission and strategic priorities. In this process, decision-makers must determine how to allocate technological resources to platforms that meet clinical and administrative needs while reducing the need for frequent replacement or reconfiguration. Advances in artificial intelligence and its capabilities add urgency and complexity to technology investment decisions. An important consideration during this process is when to build new technology infrastructure and when to partner with existing companies and buy technology solutions. This case study explores a major academic medical center's approach to that decision, including the factors that influenced it and the outcomes of two solutions that were developed in-house., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Foundation of the American College of Healthcare Executives.)

Work-related musculoskeletal disorders (WMSDs) are a major health concern in the construction industry. Occupational exoskeletons (EXOs) are a promising ergonomic intervention to help reduce WMSD risk. Their adoption, however, has been low in construction. To understand the contributing factors to EXO use-intention and assist in future decision-making, we built decision trees to predict responses to each of three EXO use-intention questions (Try, Voluntary Use, and Behavioural Intention), using online survey responses. Variable selection and hyperparameter tuning were used respectively to reduce the number of potential predictors and improve prediction performance. The importance of variables in each final tree was calculated to understand which variables had a greater influence. The final trees had moderate prediction performance. The root node of each tree included EXOs becoming standard equipment, fatigue reduction, or performance increase. Important variables were found to be quite specific to different decision trees. Practical implications of the findings are discussed. Practitioner summary: This study used decision trees to identify key factors influencing the use-intention of occupational exoskeletons (EXOs) in construction, using online survey data. Key factors identified included EXOs becoming standard equipment, fatigue reduction, and performance improvement. Final trees provide intuitive visual representations of the decision-making process for workers to use EXOs.

Objective: High plasma levels of mono- N -desethylamiodarone (MDEA), an active amiodarone metabolite, may be associated with tissue toxicity in heart failure (patients with heart rhythm disturbances); therefore, a tool that can identify patients for whom therapeutic drug monitoring (TDM) of MDEA is required. This multicenter study aimed to develop a decision tree (DT) model that can identify patients with heart rhythm disturbances at high MDEA concentrations., Materials and Methods: A multicenter retrospective cohort study was conducted, including 157 adult patients with heart failure who received oral amiodarone treatment. A χ 2 automatic interaction-detection algorithm was used to construct a DT model. In the DT analysis, the dependent variable was set as an MDEA trough plasma concentration of ≥ 0.6 μg/mL during the steady-state period. Explanatory variables were selected as factors with p < 0.05 in multivariate logistic regression analysis., Results: The adjusted odds ratios for the daily dose of amiodarone and body mass index were 1.01 (95% coefficient interval: 1.008 - 1.021, p < 0.001) and 0.91 (95% confidence interval: 0.834 - 0.988, p = 0.025), respectively. For DT analysis, the risk of reaching plasma MDEA concentrations ≥ 0.6 μg/mL was relatively high, combined with a daily dose of amiodarone > 100 mg and body mass index ≤ 22.3 kg/m 2 at 69.0% (20/29), and its trend was also detected in the sensitivity analysis., Conclusion: Patients taking a daily amiodarone dose > 100 mg and with a body mass index ≤ 22.3 kg/m 2 warrant TDM implementation for MDEA to minimize the risk of MDEA-induced tissue toxicity.

Background: Preliminary evidence suggests that meal timing is associated with higher quality diets. Less is known about whether types of food consumed during specific eating episodes (i.e., day-level eating patterns) predict diet quality., Objectives: We investigated the association between day-level eating patterns and diet quality., Methods: Decision tree models were built using 24-h dietary recall data from the National Health and Nutrition Examination Survey 2015 and 2017 cycles in a cross-sectional study. Sixteen food groups and 12 eating episodes (e.g., breakfast, lunch) were included as input parameters. Diet quality was scored using the Healthy Eating Index-2020 and categorized as higher or lower quality diets based on the median score. Mean decrease in impurity (MDI) ± standard deviation determined the relative contribution that day-level eating patterns had on diet quality; higher values represented greater contributions., Results: We analyzed 12,597 dietary recalls from 9347 United States adults who were aged 18 y and older with ≥1 complete recall. Meals (breakfast, lunch, dinner) and respective snacking episodes had the greatest variety of dietary groups that contributed to the Healthy Eating Index-2020 score. Any whole-grain intake at breakfast predicted a higher quality diet (MDI = 0.08 ± 0.00), followed by lower solid fat intake (<8.94 g; MDI = 0.07 ± 0.00) and any plant protein intake at dinner (MDI = 0.05 ± 0.00)., Conclusions: Day-level eating patterns were associated with diet quality, emphasizing the relevance of both food type and timing in relation to a high-quality diet. Future interventions should investigate the potential impact of targeting food type and timing to improve diet quality., Competing Interests: Conflict of interest The authors report no conflicts of interest., (Copyright © 2024 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Background: Coronary heart disease is the leading cause of heart failure (HF), and tools are needed to identify patients with a higher probability of developing HF after an acute coronary syndrome (ACS). Artificial intelligence (AI) has proven to be useful in identifying variables related to the development of cardiovascular complications., Methods: We included all consecutive patients discharged after ACS in two Spanish centers between 2006 and 2017. Clinical data were collected and patients were followed up for a median of 53months. Decision tree models were created by the model-based recursive partitioning algorithm., Results: The cohort consisted of 7,097 patients with a median follow-up of 53months (interquartile range: 18-77). The readmission rate for HF was 13.6% (964 patients). Eight relevant variables were identified to predict HF hospitalization time: HF at index hospitalization, diabetes, atrial fibrillation, glomerular filtration rate, age, Charlson index, hemoglobin, and left ventricular ejection fraction. The decision tree model provided 15 clinical risk patterns with significantly different HF readmission rates., Conclusions: The decision tree model, obtained by AI, identified 8 leading variables capable of predicting HF and generated 15 differentiated clinical patterns with respect to the probability of being hospitalized for HF. An electronic application was created and made available for free., (Copyright © 2024 Elsevier España, S.L.U. All rights reserved.)

Obesity is an abnormal and potentially dangerous condition caused by excess body fat accumulation. The number of people with obesity is increasing worldwide. Obesity is the primary cause of various diseases; therefore, it is crucial to make efforts to control body weight. Identifying the factors that influence men with obesity to attempt to control and not control their weight is essential. The objective of this study was to create a prediction model for weight control experience among Korean men in their 30 s and 40 s. We analyzed data from the 2022 Community Health Survey and included 12,311 men who were overweight or obese. The men were divided into two groups based on their weight control experience: (1) Yes group (n = 9405) and (2) No group (n = 2906). Chi-square and independent t-tests were used to compare general and health-related characteristics between the groups. Decision tree analysis was used to build a prediction model for weight control experience. A split-sample test was conducted to validate the model. From the results of this study, various models predicting weight control experience were derived. From the decision tree model without setting the first node, those who weighed below average, had a high school diploma or less, and did not know their blood sugar levels had the highest probability of not controlling their weight at 55.3%. In the prediction model where the first node was set to age, those in their 40 s who thought their weight was below average and were unaware of their blood sugar levels had the highest rate of not trying to control their weight at 50.1%. In the prediction model where the first node was set to BMI, those who were overweight but thought their weight was below average and had a high school diploma or less had the highest rate of not trying to control their weight at 51.5%. There is an urgent need to provide obesity prevention and management education to those who have no weight control experience, particularly those at high risk, as identified in this study., (© 2024. The Author(s).)

This study provides additional context to the literature regarding the social inequities that impact birth outcomes in Virginia using a decision tree analysis. Chi-squared automatic interaction detection data analysis (CHAID) was performed using data from the Virginia birth registry for the years 2015-2019. Birth weight was the outcome variable, while sociodemographic factors and maternity care deserts were the explanatory variables. The prevalence of low birth weight in Virginia was of 8.1%. The CHAID decision tree model demonstrated multilevel interaction among risk factors with three levels, with a total of 34 nodes. All the variables reached significance in the model, with race/ethnicity being the first major predictor variable, each category of race and ethnicity having different significant predictors, followed by prenatal care and maternal education in the next levels. These findings signify modifiable risk factors for low birth weight, in prioritizing efforts such as programs and policies. CHAID decision tree analysis provides an effective approach to detect target populations for further intervention as pathways derived from this decision tree shed light on the different predictors of high-risk population in each of the race/ethnicity demographic categories in Virginia.

Background: Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model., Methods: We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8 + programmed death-1 (PD-1)+ T cell receptor (TCR) profiles., Findings: Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality., Conclusions: The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables., Funding: This study was supported by the National Key R&D Program of China., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Depressive symptoms are frequently observed in patients with primary biliary cholangitis (PBC). The role of depressive symptoms on cirrhosis has not been fully noticed in PBC. We aimed to establish a risk model for cirrhosis that took depressive symptoms into account., Methods: Depressive symptoms were assessed by the 17-item Hamilton Depression Rating Scale (HAMD-17). HAMD-17 score was analyzed in relation to clinical parameters. Least absolute shrinkage and selection operator (Lasso)-logistic regression and decision tree models were used to explore the effect of depressive symptoms on cirrhosis., Results: The rate of depressive symptom in patients with PBC (n = 162) was higher than in healthy controls (n = 180) (52.5% vs. 16.1%; p < .001). HAMD-17 score was negatively associated with C4 levels and positively associated with levels of alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT), total bilirubin (TB), Immunoglobulin (Ig) G, and IgM (r = -0.162, 0.197, 0.355, 0.203, 0.182, 0.314, p < .05). In Lasso-logistic regression analysis, HAMD-17 score, human leukocyte antigen (HLA)-DRB1*03:01 allele, age, ALP levels, and IgM levels (odds ratio [OR] = 1.087, 7.353, 1.075, 1.009, 1.005; p < 0.05) were independent risk factors for cirrhosis. Elevated HAMD-17 score was also a discriminating factor for high risk of cirrhosis in patients with PBC in decision tree model., Conclusions: Depressive symptoms were associated with disease severity. Elevated HAMD-17 score was a risk factor for cirrhosis in patients with PBC., (© 2024 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.)

Objectives: This study was designed to investigate clinical differences between pediatric patients who presented with chest pain, tachycardia, and/or tachypnea who subsequently were or were not diagnosed with myocarditis. The results were used to develop a decision tree to aid in rapid diagnosis of pediatric myocarditis., Methods: A retrospective case-control study was performed using the electronic medical records of children aged 0 to 18 years between the years 2003 and 2020 with a complaint of chest pain, tachycardia, and/or tachypnea. Patients included in the study were those diagnosed with myocarditis and those with suspected myocarditis, which was ultimately ruled out. Demographic and clinical differences between the research groups were analyzed. A decision tree was rendered using the rpart (Recursive Partitioning and Regression Trees) package., Results: Four thousand one hundred twenty-five patients were screened for eligibility. Seventy-three myocarditis patients and 292 nonmyocarditis patients were included. Compared with the control group, the study group was found to have a higher mean respiratory rate (37 ± 23 vs 23 ± 7 breaths per minute) and mean heart rate (121 ± 44 vs 97 ± 25 beats per minute) and lower mean systolic and diastolic blood pressure (102 ± 27/56 ± 17 mm Hg vs 114 ± 14/67 ± 10 mm Hg). The mean white blood cell count was greater in the case group (13 ± 6 vs 10 ± 5 × 10 3 /μL). A decision tree was rendered using simple demographic and clinical variables. The accuracy of the algorithm was 85.2%, with 100% accuracy in patients aged 0 to 2.5 years and 69% in patients aged 2.5 to 18 years., Conclusion: The clinical and laboratory characteristics described in this study were similar to what is described in the literature. The decision tree may aid in the diagnosis of myocarditis in patients 2.5 years and younger. In the population aged 2.5 to 18 years, the decision tree did not constitute an adequate tool for detecting myocarditis., Competing Interests: Disclosure: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Many effective therapies for psoriasis are being applied in clinical practice in recent years, however, some patients still can't achieve satisfied effect even with biologics. Therefore, it is crucial to identify factors associated with the treatment efficacy among psoriasis patients. This study aims to explore factors influencing the treatment efficacy of psoriasis patients based on decision tree model and logistic regression., Methods: We implemented an observational study and recruited 512 psoriasis patients in Shanghai Skin Diseases Hospital from 2021 to 2022. We used face-to-face questionnaire interview and physical examination to collect data. Influencing factors of treatment efficacy were analyzed by using logistic regression, and decision tree model based on the CART algorithm. The receiver operator curve (ROC) was plotted for model evaluation and the statistical significance was set at P < 0.05., Results: The 512 patients were predominately males (72.1%), with a median age of 47.5 years. In this study, 245 patients achieved ≥ 75% improvement in psoriasis area and severity index (PASI) score in week 8 and was identified as treatment success (47.9%). Logistic regression analysis showed that patients with senior high school and above, without psoriasis family history, without tobacco smoking and alcohol drinking had higher percentage of treatment success in patients with psoriasis. The final decision tree model contained four layers with a total of seventeen nodes. Nine classification rules were extracted and five factors associated with treatment efficacy were screened, which indicated tobacco smoking was the most critical variable for treatment efficacy prediction. Model evaluation by ROC showed that the area under curve (AUC) was 0.79 (95%CI: 0.75 ~ 0.83) both for logistic regression model (0.80 sensitivity and 0.69 specificity) and decision tree model (0.77 sensitivity and 0.73 specificity)., Conclusion: Psoriasis patients with higher education, without tobacco smoking, alcohol drinking and psoriasis family history had better treatment efficacy. Decision tree model had similar predicting effect with the logistic regression model, but with higher feasibility due to the nature of simple, intuitive, and easy to understand., (© 2024. The Author(s).)

Background: The ideal approach for treating anterior cruciate ligament (ACL) injury is still disputed. This study aimed to determine the more cost-effective strategy by comparing early ACL reconstruction (ACLR) surgery to conservative treatment (rehabilitation with optional delayed reconstruction) for ACL injury in a lower/middle-income country (LMIC), Indonesia., Methods: A decision tree model was constructed for cost-utility analysis of early ACLR versus conservative treatment. The transition probabilities between states were obtained from the literature review. Utilities were measured by the EQ-5D-3 L from a prospective cohort study in a local hospital. The costs were obtained from a previous study that elaborated on the burden and cost of ACLR in Indonesia. Effectiveness was expressed in quality-adjusted life years gained (QALYs). Principal outcome measure was the incremental cost-effectiveness ratios (ICER). Willingness-to-pay was set at US$12,876 - three times the Indonesian GDP per capita in 2021 - the currently accepted standard in Indonesia as suggested by the World Health Organization Choosing Interventions that are Cost-Effective criterion (WHO-CHOICE)., Results: The early ACLR group showed an incremental gain of 0.05 QALYs over the conservative treatment group, with a higher overall cost to society of US$976. The ICER of ACLR surgery was US$19,524 per QALY, above the WTP threshold of US$12,876. The ICER was sensitive to cost of conservative treatment, cost of ACLR, and rate of cross-over to delayed ACLR numbers in the conservative treatment group. Using the WTP threshold of US$12,876, the probability of conservative treatment being preferred over early ACLR was 64%., Conclusions: Based on the current model, early ACLR surgery does not seem more cost-effective compared to conservative treatment for ACL injury patients in Indonesia. Because the result was sensitive to the rate of cross-over probabilities from the conservative treatment alone to delayed ACLR, a future study with a long-term perspective is needed to further elucidate its impact., (© 2024. The Author(s).)

Objective: To evaluate the effectiveness of a decision tree that integrates conventional ultrasound (CUS) with two different strain imaging (SI) techniques for diagnosing breast lesions, and to analyze the factors contributing to false negative (FN) and false positive (FP) in the decision tree's outcomes., Materials and Methods: Imaging and clinical data of 796 cases in the training set and 351 cases in the validation set were prospectively collected. A decision tree model that combines two types of SI and CUS was constructed, and its diagnostic performance was analyzed. Univariate analysis and multivariate analysis were applied to identify independent risk factors associated with FP and FN results of the decision tree model., Results: Size, shape, margin, vascularity, the types of internal calcifications, EI score and VTI pattern were found to be significantly independently associated with the diagnosis of benign and malignant breast lesions. Therefore, size, shape, margin, vascularity, EI score and VTI pattern were used to construct decision tree models. The Tree (EI+VTI) model had the highest AUC. Both in the training and validation groups, the AUC of Tree (EI+VTI) was significantly higher compared with that of EI, VTI, and BI-RADS (all, P < 0.05). Orientation, posterior acoustic features and the types of internal calcifications were significantly positively associated with misdiagnosis results of Tree (EI+VTI) in evaluation of breast lesions (all P < 0.05)., Conclusion: The diagnostic model based on a decision tree that integrates two distinct types of SI with CUS enhances the diagnostic accuracy of each method when used individually. This integration lowers the misdiagnosis rate, potentially assisting radiologists in more effective lesion assessments. When applying the decision tree model, attention should be paid to the orientation, posterior acoustic features, and the types of internal calcifications of the lesions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: Glioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual prognosis varies depending on individual prognostic factors, that must be considered while counseling patients with newly diagnosed GBM. The aim of this study was to elaborate a risk stratification algorithm based on reliable prognostic factors to facilitate a personalized prognosis estimation early on after diagnosis., Methods: A consecutive patient cohort with confirmed GBM treated between 2010 and 2021 was retrospectively analyzed. Clinical, radiological, and molecular parameters were assessed and included in the analysis. Overall survival (OS) was the primary outcome parameter. After identifying the strongest prognostic factors, a risk stratification algorithm was elaborated with estimated odds of survival., Results: A total of 462 GBM patients were analyzed. The strongest prognostic factors were Charlson Comorbidity Index (CCI), extent of tumor resection, and adjuvant treatment. Patients with CCI ≤ 1 receiving tumor resection had the highest survival odds (88% for 10 months). On the contrary, patients with CCI > 3 receiving no adjuvant treatment had the lowest survival odds (0% for 10 months). The 10-months survival rate in patients with CCI > 3 receiving adjuvant treatment was 56% for patients younger than 70 years and 22% for patients older than 70 years., Conclusion: A risk stratification algorithm based on significant prognostic factors allowed a personalized early prognosis estimation at the time of GBM diagnosis, that can contribute to a more personalized patient counseling., (© 2024. The Author(s).)

The objective of the current study was to explore the utilization of the decision tree (DT) algorithm to model posture-discomfort relationships at the individual level. The DT algorithm has the advantage that it makes no assumptions about the distribution of data, is robust in representing non-linear data with noise, and produces white-box models that are interpretable. Individual-level modelling is essential for examining individual-specific postural discomfort perception processes and understanding the inter-individual variability. It also has practical applications, including the development of individual-specific digital human models and more precise and informative population accommodation analysis. Individual-specific DT models were generated using postural discomfort rating data for various seated upper body postures to predict discomfort based on postural and task variables. The individual-specific DT models accurately predicted postural discomfort and revealed large inter-individual variability in the modelling results. DT modelling is expected to greatly facilitate investigating the human discomfort perception process., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: In the ASTRUM-004 trial, serplulimab plus chemotherapy demonstrated significantly improved survival and controllable safety. This study assessed the cost-effectiveness of serplulimab plus chemotherapy in advanced squamous non-small cell lung cancer (sqNSCLC), considering the perspective of the Chinese healthcare system., Methods: A decision tree and a Markov model were constructed to simulate the treatment. The interesting results included total cost, life-years (LYs), quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs). Scenario, one-way and probabilistic sensitivity analyses were used to examine model instability., Results: Compared with placebo plus chemotherapy, serplulimab plus chemotherapy had an ICER of $55,539.46/QALY ($47,278.84/LY). The ICERs were estimated to be $58,706.03/QALY, $48,978.34/QALY and $59,709.54/QALY inpatients with programmed death-ligand 1 expression level of tumor proportion score (TPS) < 1%, 1% ≤ TPS < 50%, and TPS ≥ 50%. The cost-effective prices of serplulimab were $168.276/100 mg, $349.157/100 mg, and $530.039/100 mg at the willingness-to-pay threshold of $12,574.30/QALY, $25,148.60/QALY, and $37,722.90/QALY. Patient weight and price of serplulimab created the most significant impact. Presently, the probability of serplulimab plus chemotherapy being cost-effective was 14.15%., Conclusion: Compared with placebo plus chemotherapy, serplulimab plus chemotherapy might not be cost-effective in the first-line treatment for advanced sqNSCLC.

Background & Aims: The extracellular water-to-total body water ratio (ECW/TBW) increases with age and after fractures. A high ECW/TBW may hinder improvements in physical function and skeletal muscle mass. However, the effects of ECW/TBW improvement have not been properly investigated. The aim of this study was to investigate the factors associated with ECW/TBW improvement in older adults with hip fractures., Methods: This retrospective cohort study included 203 patients with hip fractures who were admitted to a convalescent rehabilitation ward. ECW/TBW and skeletal muscle mass index (SMI) were measured using bioelectrical impedance analysis. The patients were classified into two groups: those with an improvement in ECW/TBW (n = 123) and those without an improvement (n = 80). Decision tree analysis was performed to examine the factors associated with ECW/TBW improvement. As a secondary objective, a multiple regression analysis was performed to identify the factors associated with SMI gain., Results: Decision tree analysis identified rehabilitation volume and protein intake as the first and second factors most significantly associated with an improvement in ECW/TBW, respectively. Multiple regression analysis showed that improved ECW/TBW (β: 0.400, p < 0.001) was significantly associated with SMI gain., Conclusions: Rehabilitation volume and protein intake are clinically important for improving ECW/TBW in older adults with hip fractures., Competing Interests: Declaration of competing interest The authors declare no competing interest., (Copyright © 2024 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.)

Purpose: Identifying predictors of opioid overdose following release from prison is critical for opioid overdose prevention., Methods: We leveraged an individually linked, state-wide database from 2015-2020 to predict the risk of opioid overdose within 90 days of release from Massachusetts state prisons. We developed two decision tree modeling schemes: a model fit on all individuals with a single weight for those that experienced an opioid overdose and models stratified by race/ethnicity. We compared the performance of each model using several performance measures and identified factors that were most predictive of opioid overdose within racial/ethnic groups and across models., Results: We found that out of 44,246 prison releases in Massachusetts between 2015-2020, 2237 (5.1%) resulted in opioid overdose in the 90 days following release. The performance of the two predictive models varied. The single weight model had high sensitivity (79%) and low specificity (56%) for predicting opioid overdose and was more sensitive for White non-Hispanic individuals (sensitivity = 84%) than for racial/ethnic minority individuals., Conclusions: Stratified models had better balanced performance metrics for both White non-Hispanic and racial/ethnic minority groups and identified different predictors of overdose between racial/ethnic groups. Across racial/ethnic groups and models, involuntary commitment (involuntary treatment for alcohol/substance use disorder) was an important predictor of opioid overdose., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Joshua A. Barocas reports financial support was provided by National Institutes of Health. Prasad Patil, Kristina Yamkovoy, Samantha K. Nall, Pallavi Aytha Swathi, Lauren Brinkley-Rubinstein reports financial support was provided by National Institutes of Health. Laura F. White, Prasad Patil, Yanjia Zhang reports financial support was provided by National Institute of General Medical Sciences. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: Colonoscopy is a critical diagnostic tool for colorectal diseases; however, its effectiveness depends on adequate bowel preparation (BP). This study aimed to develop a machine learning predictive model based on Chinese adults for inadequate BP., Methods: A multicenter prospective study was conducted on adult outpatients undergoing colonoscopy from January 2021 to May 2023. Data on patient characteristics, comorbidities, medication use, and BP quality were collected. Logistic regression and 4 machine learning models (support vector machines, decision trees, extreme gradient boosting, and bidirectional projection network) were used to identify risk factors and predict inadequate BP., Results: Of 3,217 patients, 21.14% had inadequate BP. The decision trees model demonstrated the best predictive capacity with an area under the receiver operating characteristic curve of 0.80 in the validation cohort. The risk factors at the nodes included body mass index, education grade, use of simethicone, diabetes, age, history of inadequate BP, and longer interval., Discussion: The decision trees model we created and the identified risk factors can be used to identify patients at higher risk of inadequate BP before colonoscopy, for whom more polyethylene glycol or auxiliary medication should be used., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Abstract: Current hospital venous thromboembolism (VTE) prophylaxis for medical patients is characterized by both underuse and overuse. The American Society of Hematology (ASH) has endorsed the use of risk assessment models (RAMs) as an approach to individualize VTE prophylaxis by balancing overuse (excessive risk of bleeding) and underuse (risk of avoidable VTE). ASH has endorsed IMPROVE (International Medical Prevention Registry on Venous Thromboembolism) risk assessment models, the only RAMs to assess short-term bleeding and VTE risk in acutely ill medical inpatients. ASH, however, notes that no RAMs have been thoroughly analyzed for their effect on patient outcomes. We aimed to validate the IMPROVE models and adapt them into a simple, fast-and-frugal (FFT) decision tree to evaluate the impact of VTE prevention on health outcomes and costs. We used 3 methods: the "best evidence" from ASH guidelines, a "learning health system paradigm" combining guideline and real-world data from the Medical University of South Carolina (MUSC), and a "real-world data" approach based solely on MUSC data retrospectively extracted from electronic records. We found that the most effective VTE prevention strategy used the FFT decision tree based on an IMPROVE VTE score of ≥2 or ≥4 and a bleeding score of <7. This method could prevent 45% of unnecessary treatments, saving ∼$5 million annually for patients such as the MUSC cohort. We recommend integrating IMPROVE models into hospital electronic medical records as a point-of-care tool, thereby enhancing VTE prevention in hospitalized medical patients., (© 2024 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Background: While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months., Materials and Methods: We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated., Results: A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points., Conclusions: Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The brain biomarker of irritable bowel syndrome (IBS) patients is still lacking. The study aims to explore a new technology studying the brain alterations of IBS patients based on multi-source brain data. In the study, a decision-level fusion method based on gradient boosting decision tree (GBDT) was proposed. Next, 100 healthy subjects were used to validate the effectiveness of the method. Finally, the identification of brain alterations and the pain evaluation in IBS patients were carried out by the fusion method based on the resting-state fMRI and DWI for 46 patients and 46 controls selected randomly from 100 healthy subjects. The results showed that the method can achieve good classification between IBS patients and controls (accuracy = 95%) and pain evaluation of IBS patients (mean absolute error = 0.1977). Moreover, both the gain-based and the permutation-based evaluation instead of statistical analysis showed that left cingulum bundle contributed most significantly to the classification, and right precuneus contributed most significantly to the evaluation of abdominal pain intensity in the IBS patients. The differences seem to suggest a probable but unexplored separation about the central regions between the identification and progression of IBS. This finding may provide one new thought and technology for brain alteration related to IBS., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

Across the nation, patients with locally advanced gastric cancer (LAGC) are managed with modalities including upfront surgery (US) and perioperative chemotherapy (PCT). Preoperative therapies have demonstrated survival benefits over US and thus long-term outcomes are expected to vary between the options. However, as these 2 modalities continue to be regularly employed, we sought to perform a decision analysis comparing the costs and quality-of-life associated with the treatment of patients with LAGC to identify the most cost-effective option. We designed a decision tree model to investigate the survival and costs associated with the most commonly utilized management modalities for LAGC in the United States: US and PCT. The tree described costs and treatment strategies over a 6-month time horizon. Costs were derived from 2022 Medicare reimbursement rates using the third-party payer perspective for physicians and hospitals. Effectiveness was represented using quality-adjusted life-years (QALYs). One-way, two-way, and probabilistic sensitivity analyses were utilized to test the robustness of our findings. PCT was the most cost-effective treatment modality for patients with LAGC over US with a cost of $40,792.16 yielding 3.11 QALYs. US has a cost of $55,575.57 while yielding 3.15 QALYs; the incremental cost-effectiveness ratio (ICER) was $369,585.25. One-way and two-way sensitivity analyses favored PCT in all variations of variables across their standard deviations. Across 100,000 Monte Carlo simulations, 100% of trials favored PCT. In our model simulating patients with LAGC, the most cost-effective treatment strategy was PCT. While US demonstrated improved QALYs over PCT, the associated cost was too great to justify its use., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Life cycle assessment (LCA) plays a crucial role in green manufacturing to uncover the critical aspects for alleviating the environmental burdens due to manufacturing processes. However, the scarcity of life cycle inventory (LCI) data for the manufacturing processes is a considerable challenge. This paper proposes a novel approach to extrapolate LCI data of manufacturing processes. Taking advantage of LCI data in the Ecoinvent datasets, decision tree-based supervised machine learning models, namely decision tree, random forest, gradient boosting, and adaptive boosting, have been developed to extrapolate the data of GHG emissions, i.e., carbon dioxide, nitrous oxide, methane, and water vapor. Initially, a correlation analysis was conducted to derive the most influential factors on GHG quantities resulting from manufacturing activities. First, the collected data have been preprocessed and split into train and test sets (70% and 30%, respectively). Second, a five-fold cross-validation method was applied to tune the hyperparameters of the models. Then, the models were re-trained using the best hyperparameters and evaluated using the test set. The results reveal that the Gradient Boosting model has a superior predictive performance for extrapolating the GHG emission data, with average coefficients of determination (R 2 ) on the test set <0.95. Moreover, the model predictions involve relatively low values of the average root mean squared error and an average mean percentage of error on the test set. The correlation and feature importance analyses emphasized that the workpiece material and manufacturing technology have a considerable effect on natural resource consumption, i.e., energy, material, and water inflows into the process. Meanwhile, energy consumption, water usage, and raw aluminum depletion were the most influential factors in GHG emissions. Eventually, a case study to extrapolate the inflows and the outflows for new manufacturing activities has been conducted using the validated models. The proposed GraBoost model provides a computational supplementary approach to estimate and extrapolate the GHG emissions for different manufacturing processes when LCI data are incomplete or don't exist within LCI databases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objectives: To meet the demand for personalized treatment, effective stratification of patients with metastatic nasopharyngeal carcinoma (mNPC) is essential. Hence, our study aimed to establish an M1 subdivision for prognostic prediction and treatment planning in patients with mNPC., Materials and Methods: This study included 1239 patients with mNPC from three medical centers divided into the synchronous mNPC cohort (smNPC, n = 556) to establish an M1 stage subdivision and the metachronous mNPC cohort (mmNPC, n = 683) to validate this subdivision. The primary endpoint was overall survival. Univariate and multivariate Cox analyses identified covariates for the decision-tree model, proposing an M1 subdivision. Model performance was evaluated using time-dependent receiver operating characteristic curves, Harrell's concordance index, calibration plots, and decision curve analyses., Results: The proposed M1 subdivisions were M1a (≤5 metastatic lesions), M1b (>5 metastatic lesions + absent liver metastases), and M1c (>5 metastatic lesions + existing liver metastases) with median OS of 34, 22, and 13 months, respectively (p < 0.001). This M1 subdivision demonstrated superior discrimination (C-index = 0.698; 3-year AUC = 0.707) and clinical utility over those of existing staging systems. Calibration curves exhibited satisfactory agreement between predictions and actual observations. Internal and mmNPC cohort validation confirmed the robustness. Survival benefits from local metastatic treatment were observed in M1a, while immunotherapy improved survival in patients with M1b and M1c disease., Conclusion: This novel M1 staging strategy provides a refined approach for prognostic prediction and treatment planning in patients with mNPC, emphasizing the potential benefits of local and immunotherapeutic interventions based on individualized risk stratification., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Previously, a lexicon and protocol for quantitative descriptive analysis (QDA) was established for the Uganda sweetpotato breeding program. The implication of QDA scores for priority sensory attributes on consumer preference should be determined to interpret results efficiently and make decisions effectively. The present study aimed to develop a gender-responsive decision tree to obtain an overall sweetpotato eating quality score to facilitate demand-led targeted breeding selection. It focused on Kamuli and Hoima districts (Uganda) and uses pre-lease advanced clones ('NKB3', 'NKB105', 'NKB135', 'D11' and 'D20'), released varieties ('NASPOT 8' and 'NAROSPOT 1') and landraces ('Muwulu-Aduduma', 'Umbrella')., Results: Including boiled sweetpotato sensory characteristics, namely mealy, sweet taste, sweetpotato smell, firm and not fibrous, in breeding design would benefit end-users, especially women given their role in varietal selection, food preparation and marketing. 'D20', 'NASPOT 8' and 'NAROSPOT 1' were most liked in both districts. 'NKB3' and 'D11' were the least liked in Hoima, whereas 'Muwulu-Aduduma' was the least liked in Kamuli. There was a positive correlation between color and overall liking (r 2  = 0.8) and consumers liked the color (average rating ≥ 6 on a nine-point hedonic scale) of all genotypes. Threshold values (average rating on 11-point scales) for consumer acceptability were identified (sweet taste = 6, sweetpotato aroma and flavor = 6, firmness = 3, and mealiness = 4). A regression decision tree tool was created to calculate an eating quality selection index when screening lines in breeding programs using the values., Conclusion: Decision trees that include consumer needs and gender considerations would facilitate demand-led breeding and make varietal selection in sweetpotato breeding programs more effective. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

Purpose: This study aimed to examine pacing strategies and identify the stroke that has the most significant impact on overall performance in men's and women's 200-m and 400-m individual-medley events from 2000 to 2021., Methods: The time in each lap and overall race was retrieved from the World Aquatics website. The standardized time for each stroke in individual medley was calculated by dividing the actual time by a reference time specific to each stroke. The reference time was derived from the respective laps in single-stroke finals in the 2017 World Swimming Championships. The decision-tree method was used for analysis. The dependent variables were qualified or nonqualified in heats and semifinals, and winning medals in finals. The independent variables were the ratio of standardized time in each stroke to the sum of standardized time in all 4 strokes., Results: Swimmers who spent a higher ratio of standardized time in the butterfly stroke (>0.236-0.245) are associated with a higher likelihood of winning medals or qualifying for the next stage in most men's and women's 200-m and 400-m individual medley. Butterfly exhibited the highest normalized importance that distinguished medalists from nonmedalists in the finals. The front-crawl stroke is the second most important determinant in medalists in men's and women's 200-m individual medley, whereas backstroke and breaststroke were the second most important in men's and women's 400-m individual medley, respectively., Conclusion: Individual-medley swimmers who were excellent in butterfly and conserved energy in butterfly had a higher likelihood of success.

Background: Perinatal depression affects the physical and mental health of pregnant women. It also has a negative effect on children, families, and society, and the incidence is high. We constructed a cost-utility analysis model for perinatal depression screening in China and evaluated the model from the perspective of health economics., Methods: We constructed a Markov model that was consistent with the screening strategy for perinatal depression in China, and two screening strategies (screening and non-screening) were constructed. Each strategy was set as a cycle of 3 months, corresponding to the first trimester, second trimester, third trimester, and postpartum. The state outcome parameters required for the model were obtained based on data from the National Prospective Cohort Study on the Mental Health of Chinese Pregnant Women from August 2015 to October 2016. The cost parameters were obtained from a field investigation on costs and screening effects conducted in maternal and child health care institutions in 2020. The cost-utility ratio and incremental cost-utility ratio of different screening strategies were obtained by multiplicative analysis to evaluate the health economic value of the two screening strategies. Finally, deterministic and probabilistic sensitivity analyses were conducted on the uncertain parameters in the model to explore the sensitivity factors that affected the selection of screening strategies., Results: The cost-utility analysis showed that the per capita cost of the screening strategy was 129.54 yuan, 0.85 quality-adjusted life years (QALYs) could be obtained, and the average cost per QALY gained was 152.17 yuan. In the non-screening (routine health care) group, the average cost was 171.80 CNY per person, 0.84 QALYs could be obtained, and the average cost per QALY gained was 205.05 CNY. Using one gross domestic product per capita in 2021 as the willingness to pay threshold, the incremental cost-utility ratio of screening versus no screening (routine health care) was about -3,126.77 yuan, which was lower than one gross domestic product per capita . Therefore, the screening strategy was more cost-effective than no screening (routine health care). Sensitivity analysis was performed by adjusting the parameters in the model, and the results were stable and consistent, which did not affect the choice of the optimal strategy., Conclusion: Compared with no screening (routine health care), the recommended perinatal depression screening strategy in China is cost-effective. In the future, it is necessary to continue to standardize screening and explore different screening modalities and tools suitable for specific regions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yang, Zheng, Yang, Huang and Zhang.)

According to the European Society of Cardiology, globally the number of patients with heart failure nearly doubled from 33.5 million in 1990 to 64.3 million in 2017, and is further projected to increase dramatically in this decade, still remaining a leading cause of morbidity and mortality. One of the most frequently applied heart failure classification systems that physicians use is the New York Heart Association (NYHA) Functional Classification. Each NYHA class describes a patient's symptoms while performing physical activities, delivering a strong indicator of the heart performance. In each case, a NYHA class is individually determined routinely based on the subjective assessment of the treating physician. However, such diagnosis can suffer from bias, eventually affecting a valid assessment. To tackle this issue, we take advantage of the machine learning approach to develop a decision-tree, along with a set of decision rules, which can serve as additional blinded investigator tool to make unbiased assessment. On a dataset containing 434 observations, the supervised learning approach was initially employed to train a Decision Tree model. In the subsequent phase, ensemble learning techniques were utilized to develop both the Voting Classifier and the Random Forest model. The performance of all models was assessed using 10-fold cross-validation with stratification.The Decision Tree, Random Forest, and Voting Classifier models reported accuracies of 76.28%, 96.77%, and 99.54% respectively. The Voting Classifier led in classifying NYHA I and III with 98.7% and 100% accuracy. Both Random Forest and Voting Classifier flawlessly classified NYHA II at 100%. However, for NYHA IV, Random Forest achieved a perfect score, while the Voting Classifier reported 90%. The Decision Tree showed the least effectiveness among all the models tested. In our opinion, the results seem satisfactory in terms of their supporting role in clinical practice. In particular, the use of a machine learning tool could reduce or even eliminate the bias in the physician's assessment. In addition, future research should consider testing other variables in different datasets to gain a better understanding of the significant factors affecting heart failure., (© 2024. The Author(s).)

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: An accurate prognostic assessment is pivotal to adequately inform and individualize follow-up and management of patients with differentiated thyroid cancer (DTC). We aimed to develop a predictive model for recurrent disease in DTC patients treated by surgery and 131 I by adopting a decision tree model., Methods: Age, sex, histology, T stage, N stage, risk classes, remnant estimation, thyroid-stimulating hormone (TSH), thyroglobulin (Tg), administered 131 I activities and post-therapy whole body scintigraphy (PT-WBS) were identified as potential predictors and put into regression algorithm (conditional inference tree, c-tree) to develop a risk stratification model for predicting persistent/recurrent disease over time., Results: The PT-WBS pattern identified a partition of the population into two subgroups (PT-WBS positive or negative for distant metastases). Patients with distant metastases exhibited lower disease-free survival (either structural, DFS-SD, and biochemical, DFS-BD, disease) compared to those without metastases. Meanwhile, the latter were further stratified into three risk subgroups based on their Tg values. Notably, Tg values >63.1 ng/mL predicted a shorter survival time, with increased DFS-SD for Tg values <63.1 and <8.9 ng/mL, respectively. A comparable model was generated for biochemical disease (BD), albeit different DFS were predicted by slightly different Tg cutoff values (41.2 and 8.8 ng/mL) compared to DFS-SD., Conclusions: We developed a simple, accurate and reproducible decision tree model able to provide reliable information on the probability of structurally and/or biochemically persistent/relapsed DTC after a TTA. In turn, the provided information is highly relevant to refine the initial risk stratification, identify patients at higher risk of reduced structural and biochemical DFS, and modulate additional therapies and the relative follow-up., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

Objective: This study aimed to investigate the predictive functional factors influencing the acquisition of basic activities of daily living performance abilities during the early stages of stroke rehabilitation using classification and regression analysis trees., Methods: The clinical data of 289 stroke patients who underwent rehabilitation during hospitalization (164 males; mean age: 62.2 ± 13.9 years) were retrospectively collected and analysed. The follow-up period between admission and discharge was approximately 6 weeks. Medical records, including demographic characteristics and various functional assessments with item scores, were extracted. The modified Barthel Index on discharge served as the target outcome for analysis. A "good outcome" was defined as a modified Barthel Index score ≥ 75 on discharge, while a modified Barthel Index score < 75 was classified as a "poor outcome.", Results: Two classification and regression analysis tree models were developed. The first model, predicting activities of daily living outcomes based on early motor functions, achieved an accuracy of 92.4%. Among patients with a "good outcome", 70.9% exhibited (i) ≥ 4 points in the "sitting-to-standing" category in the motor assessment scale and (ii) 32 points on the Berg Balance Scale score. The second model, predicting activities of daily living outcome based on early cognitive functions, achieved an accuracy of 82.7%. Within the "poor outcome" group, 52.2% had (i) ≤ 21 points in the "visuomotor organization" category of Lowenstein Occupational Therapy Cognitive Assessment, (ii) ≤ 1 point in the "time orientation" category of the Mini Mental State Examination., Conclusion: The ability to perform "sitting-to-standing" and visuomotor organization functions at the beginning of rehabilitation emerged as the most significant predictors for achieving successful basic activities of daily living on discharge after stroke.

Background: Cognitive dysfunction is one of the leading causes of disability and dependence in older adults and is a major economic burden on the public health system. The aim of this study was to investigate the risk factors for cognitive dysfunction and their predictive value in older adults in Northwest China., Methods: A cross-sectional study was conducted using a multistage sampling method. The questionnaires were distributed through the Elderly Disability Monitoring Platform to older adults aged 60 years and above in Northwest China, who were divided into cognitive dysfunction and normal cognitive function groups. In addition to univariate analyses, logistic regression and decision tree modelling were used to construct a model to identify factors that can predict the occurrence of cognitive dysfunction in older adults., Results: A total of 12,494 valid questionnaires were collected, including 2617 from participants in the cognitive dysfunction group and 9877 from participants in the normal cognitive function group. Univariate analysis revealed that ethnicity, BMI, age, educational attainment, marital status, type of residence, residency status, current work status, main economic source, type of chronic disease, long-term use of medication, alcohol consumption, participation in social activities, exercise status, social support, total scores on the Balanced Test Assessment, total scores on the Gait Speed Assessment total score, and activities of daily living (ADL) were significantly different between the two groups (all P < 0.05). According to logistic regression analyses, ethnicity, BMI, educational attainment, marital status, residency, main source of income, chronic diseases, annual medical examination, alcohol consumption, exercise status, total scores on the Balanced Test Assessment, and activities of daily living (ADLs) were found to influence cognitive dysfunction in older adults (all P < 0.05). In the decision tree model, the ability to perform activities of daily living was the root node, followed by total scores on the Balanced Test Assessment, marital status, educational attainment, age, annual medical examination, and ethnicity., Conclusions: Traditional risk factors (including BMI, literacy, and alcohol consumption) and potentially modifiable risk factors (including balance function, ability to care for oneself in daily life, and widowhood) have a significant impact on the increased risk of cognitive dysfunction in older adults in Northwest China. The use of decision tree models can help health care workers better assess cognitive function in older adults and develop personalized interventions. Further research could help to gain insight into the mechanisms of cognitive dysfunction and provide new avenues for prevention and intervention., (© 2024. The Author(s).)