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Research has demonstrated concussion likely causes autonomic dysfunction leading to exercise intolerance. To measure improvement in exercise intolerance due to suspected dysautonomia associated with protracted concussion recovery, using objective measurements on a Buffalo Concussion Treadmill Test (BCTT) following participation in a prescribed exercise program. This is a retrospective cohort study of 101 patient charts post-concussion. Exercise intolerance was assessed using a BCTT to identify suspected dysautonomia and an exercise prescription was provided using guidelines for treating concussion-associated exercise intolerance. Patients without symptom improvement and/or inability to achieve 80–85% of age-expected maximum heart rate (HR) without symptom exacerbation received a repeat BCTT. Twelve patient charts met inclusion criteria and were included in data analysis. There were significant improvements from pre-intervention to post-intervention testing in: maximum BCTT stage mean scores (p =.02); maximum HR mean scores (p =.01); prescription HR (RxHR) mean scores (p =.01); and HR delta (HR $$\delta $$ δ) mean scores (p =.00). Maximum stage, HR threshold, RxHR, and newly identified HR delta (HR $$\delta $$ δ) are potential objective measurements of progress for dysautonomia treatment post-concussion. Future studies are indicated to create a tailored protocol in the management of protracted concussion-associated dysautonomia. [ABSTRACT FROM AUTHOR]

Background: Mainly due to their cultural attitudes toward symptoms and breast cancer, Asian American breast cancer survivors tend to suffer from symptoms and often delay in getting treatments, information, and support. To improve their symptom management, it would be important to determine risk groups among them. Decision tree analyses reportedly help determine risk groups by identifying the characteristics that are directly associated with target health outcomes. Objective: Using a decision tree analysis, this study aimed at identifying the characteristics that were closely linked to the symptom experience of Asian American breast cancer survivors. Methods: This was a part of a parent randomized controlled trial among Asian American breast cancer survivors. Only the data from 135 women at the pre-test were included. Multiple instruments were used to collect the data: the Memorial Symptom Assessment Scale-Short Form, the Cancer Behavior Inventory, the PRQ-2000, the Perceived Isolation Scale, and the Supportive Care Needs Survey-Short Form 34. The data were analyzed using latent profile analysis and decision tree analyses. Results: Two most frequently found profiles included the low symptom experience profile (72.6%) and the high symptom experience profile (27.4%). The high symptom experience profile was predicted by 2 combined characteristics; (a) high psychological needs for help (over 60.00 points), and (b) low psychological needs for help (cut point = 60.00), high perceived barriers (cut point = 1.62), and high social isolation (social support) (cut point = 2.33). Conclusions: These characteristics linked to Asian American breast cancer survivors with high symptom experience need to be considered in future intervention development. [ABSTRACT FROM AUTHOR]

Bupropion is widely used for the treatment of major depressive disorder and for smoking cessation assistance. Unfortunately, there are no practical systems to assist clinicians or poison centres in predicting outcomes based on clinical features. Hence, the purpose of this study was to use a decision tree approach to inform early diagnosis of outcomes secondary to bupropion overdose. This study utilized a dataset from the National Poison Data System, a 6‐year retrospective cohort study on toxic exposures and patient outcomes. A machine learning algorithm (decision tree) was applied to the dataset using the sci‐kit‐learn library in Python. Shapley Additive exPlanations (SHAP) were used as an explainable method. Comparative analysis was performed using random forest (RF), Gradient Boosting classification, eXtreme Gradient Boosting, Light Gradient Boosting (LGM) and voting ensembling. ROC curve and precision–recall curve were used to analyse the performance of each model. LGM and RF demonstrated the highest performance to predict outcome of bupropion exposure. Multiple seizures, conduction disturbance, intentional exposure, and confusion were the most influential factors to predict the outcome of bupropion exposure. Coma and seizure, including single, multiple and status, were the most important factors to predict major outcomes. [ABSTRACT FROM AUTHOR]

The article focuses on evolution of legal education and the profession's call for more experiential education and the use of decision trees in the clinical setting and incorporating new technology into the practice of law of bankruptcy. It mentions decision tree technology can reduce professional error and exposure to professional liability. It also mentions assistance of supervision of students, improve risk management, promote access to justice and foster judicial economy.

Abstract: Guidance for determining the sensitizing potential of chemicals is available in EC Regulation No. 1272/2008 Classification, Labeling, and Packaging of Substances; REACH guidance from the European Chemicals Agency; and the United Nations Globally Harmonized System (GHS). We created decision trees for evaluating potential skin and respiratory sensitizers. Our approach (1) brings all the regulatory information into one brief document, providing a step-by-step method to evaluate evidence that individual chemicals or mixtures have sensitizing potential; (2) provides an efficient, uniform approach that promotes consistency when evaluations are done by different reviewers; (3) provides a standard way to convey the rationale and information used to classify chemicals. We applied this approach to more than 50 chemicals distributed among 11 evaluators with varying expertise. Evaluators found the decision trees easy to use and recipients (product stewards) of the analyses found that the resulting documentation was consistent across users and met their regulatory needs. Our approach allows for transparency, process management (e.g., documentation, change management, version control), as well as consistency in chemical hazard assessment for REACH, EC Regulation No. 1272/2008 Classification, Labeling, and Packaging of Substances and the GHS. [Copyright &y& Elsevier]

Aim: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. Materials and Methods: Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine‐learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K‐nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results: The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis‐related molar loss. In addition, the ensembled model showed the best calibration performance. Conclusions: Through a multi‐centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice. [ABSTRACT FROM AUTHOR]

Background and Objective: Computer-based neurocognitive tests are widely used in sport-related concussion management, but the performance of these tests is not well understood in the participant population with attention-deficit/hyperactivity disorder (ADHD) and/or learning disorder (LD). This research estimates the sensitivity and specificity performance of the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) computer-based neurocognitive test in identifying concussion in this population. Methods: Study participants consisted of collegiate university athletes and military service academy cadets from the National Collegiate Athletic Association-Department of Defense CARE Consortium who completed the ImPACT test between 2014 and 2021. Participants who self-identified as belonging to one of the subgroups of interest (ADHD with or without LD [ADHD:LD+/−], LD with or without ADHD [LD:ADHD+/−], ADHD and/or LD [ADHD a/o LD]) and completed a baseline (1874 ADHD:LD+/−, 779 LD:ADHD+/−, 2338 ADHD a/o LD) or 24–48 h post-concussion (175 ADHD:LD+/−, 77 LD:ADHD+/−, 216 ADHD a/o LD) ImPACT assessment were included. Sensitivity and specificity were calculated using a normative data method and three machine learning classification methods: logistic regression, classification and regression tree, and random forest. Results: Using the four methods, participants with ADHD:LD+/− had sensitivities that ranged from 0.474 to 0.697, and specificities that ranged from 0.538 to 0.686. Participants with LD:ADHD+/− had sensitivities that ranged from 0.455 to 0.688, and specificities that ranged from 0.456 to 0.588. For participants with ADHD a/o LD, sensitivities ranged from 0.542 to 0.755, and specificities ranged from 0.451 to 0.724. Conclusions: For all subgroups and analytical methods, the results illustrate sensitivity and specificity values below typically accepted levels indicative of clinical utility. These findings support that using ImPACT alone may be insufficient to inform concussion diagnoses and encourages the use of a multi-dimensional concussion assessment. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: Critics have charged that evidence‐based medicine (EBM) overemphasises algorithmic rules over unstructured clinical experience and intuition, but the role of structured decision support systems in improving health outcomes remains uncertain. We aim to assess if delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to an algorithm based on evidence‐based clinical practice guideline (CPG) improved clinical outcomes compared with administration of anticoagulant treatment given at individual practitioners' discretion. Methods: An observational design consisting of the analysis of all acutely ill, consecutive patients (n = 1783) with confirmed COVID‐19 diagnosis admitted between 10 March 2020 to 11 January 2022 to an US academic center. American Society of Haematology CPG for anticoagulant prophylaxis in hospitalised patients with COVID‐19 was converted into a clinical pathway and translated into fast‐and‐frugal decision (FFT) tree ('algorithm'). We compared delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to the FFT algorithm with administration of anticoagulant treatment given at individual practitioners' discretion. Results: In an adjusted analysis, using combination of Lasso (least absolute shrinkage and selection operator) and propensity score based weighting [augmented inverse‐probability weighting] statistical techniques controlling for cluster data, the algorithm did not reduce death, venous thromboembolism, or major bleeding, but helped avoid longer hospital stay [number of patients needed to be treated (NNT) = 40 (95% CI: 23–143), indicating that for every 40 patients (23–143) managed on FFT algorithm, one avoided staying in hospital longer than 10 days] and averted admission to intensive‐care unit (ICU) [NNT = 19 (95% CI: 13–40)]. All model's selected covariates were well balanced. The results remained robust to sensitivity analyses used to test the stability of the findings. Conclusions: When delivered using a structured FFT algorithm, CPG shortened the hospital stay and help avoided admission to ICU, but it did not affect other relevant outcomes. [ABSTRACT FROM AUTHOR]

Background The purpose of this study was to estimate the health impact of syphilis in the United States in terms of the number of quality-adjusted life years (QALYs) lost attributable to infections in 2018. Methods We developed a Markov model that simulates the natural history and management of syphilis. The model was parameterized by sex and sexual orientation (women who have sex with men, men who have sex with women [MSW], and men who have sex with men [MSM]), and by age at primary infection. We developed a separate decision tree model to quantify health losses due to congenital syphilis. We estimated the average lifetime number of QALYs lost per infection, and the total expected lifetime number of QALYs lost due to syphilis acquired in 2018. Results We estimated the average number of discounted lifetime QALYs lost per infection as 0.09 (95% uncertainty interval [UI].03–.19). The total expected number of QALYs lost due to syphilis acquired in 2018 was 13 349 (5071–31 360). Although per-case loss was the lowest among MSM (0.06), MSM accounted for 47.7% of the overall burden. For each case of congenital syphilis, we estimated 1.79 (1.43–2.16) and 0.06 (.01–.14) QALYs lost in the child and the mother, respectively. We projected 2332 (1871–28 250) and 79 (17–177) QALYs lost for children and mothers, respectively, due to congenital syphilis in 2018. Conclusions Syphilis causes substantial health losses in adults and children. Quantifying these health losses in terms of QALYs can inform cost-effectiveness analyses and can facilitate comparisons of the burden of syphilis to that of other diseases. [ABSTRACT FROM AUTHOR]

Using a sample of 18,152 pediatric hospice patients, this study assessed the cost-effectiveness of concurrent care over standard hospice care. Analysis of incremental cost-effectiveness ratios with bootstrapping simulations showed concurrent care was more effective but at a higher cost. [ABSTRACT FROM AUTHOR]

This paper proposes using phasor measurement unit (PMU) data to trigger one-shot control in response to loss of synchronism detection. Decision trees (DTs) are trained to associate voltage magnitude and angle measurements with loss of synchronism. Different approaches to the DT construction process are illustrated in an early section of the paper by restricting the input vector to only two elements. Response-based control is obtained by triggering one-shot control the first time the DT classifies measurements as “Unstable”. The same combination of one-shot controls is applied to any event the first time the DT outputs “Unstable”. The method is demonstrated on a 176-bus reduced-order model of the western U.S. interconnection. An effective one-shot control consists of a combination of fast power changes on four buses. This control is similar to 500-MW fast power increases on two HVDC interties and it reduces angle differences in the AC network. [ABSTRACT FROM PUBLISHER]

Homelessness has been a persistent social concern in the United States. A combination of political and economic events since the 1960s has driven increases in poverty that, by 1991, had surpassed 1928 depression era levels in some accounts. This paper explores how the emerging field of behavioral economics can use machine learning and data science methods to explore preventative responses to homelessness. In this study, machine learning data mining strategies, specifically K-means cluster analysis and later, decision trees, were used to understand how environmental factors and resultant behaviors can contribute to the experience of homelessness. Prevention of the first homeless event is especially important as studies show that if a person has experienced homelessness once, they are 2.6 times more likely to have another homeless episode. Study findings demonstrate that when someone is at risk for not being able to pay utility bills at the same time as they experience challenges with two or more of the other social determinants of health, the individual is statistically significantly more likely to have their first homeless event. Additionally, for men over 50 who are not in the workforce, have a health hardship, and experience two or more other social determinants of health hardships at the same time, the individual has a high statistically significant probability of experiencing homelessness for the first time. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: We evaluated the economic impact of routine childhood immunization in the United States, reflecting updated vaccine recommendations and recent data on epidemiology and coverage rates. METHODS: An economic model followed the 2017 US birth cohort from birth through death; impact was modeled via a decision tree for each of the vaccines recommended for children by the Advisory Committee on Immunization Practices as of 2017 (with annual influenza vaccine considered in scenario analysis). Using information on historic prevaccine and vaccine-era incidence and disease costs, we calculated disease cases, deaths, disease-related healthcare costs, and productivity losses without and with vaccination, as well as vaccination program costs. We estimated cases and deaths averted because of vaccination, life-years and quality-adjusted life-years gained because of vaccination, incremental costs (2019 US dollars), and the overall benefit-cost ratio (BCR) of routine childhood immunization from the societal and healthcare payer perspectives. RESULTS: Over the cohort's lifetime, routine childhood immunization prevented over 17 million cases of disease and 31 000 deaths; 853 000 life years and 892 000 quality-adjusted life-years were gained. Estimated vaccination costs ($8.5 billion) were fully offset by the $63.6 billion disease-related averted costs. Routine childhood immunization was associated with $55.1 billion (BCR of 7.5) and $13.7 billion (BCR of 2.8) in averted costs from a societal and healthcare payer perspective, respectively. CONCLUSIONS: In addition to preventing unnecessary morbidity and mortality, routine childhood immunization is cost-saving. Continued maintenance of high vaccination coverage is necessary to ensure sustained clinical and economic benefits of the vaccination program. [ABSTRACT FROM AUTHOR]

The present study compared the performance of logistic regression models with that of machine learning classification models (classification trees and random forests) in the context of predicting training attrition from the Delayed Enlistment Program in the United States Marine Corps (USMC) with scores from the Tailored Adaptive Personality Assessment System (TAPAS). Performance was assessed according to the type of misclassification error and across a variety of different reasons for attrition. The base rate of attrition was low, which impeded the training process, but the machine learning models outperformed logistic regression in predicting voluntary attrition in a stratified 50% attrition sample. [ABSTRACT FROM AUTHOR]

Acetaminophen is one of the most commonly used analgesic drugs in the United States. However, the outcomes of acute acetaminophen overdose might be very serious in some cases. Therefore, prediction of the outcomes of acute acetaminophen exposure is crucial. This study is a 6‐year retrospective cohort study using National Poison Data System (NPDS) data. A decision tree algorithm was used to determine the risk predictors of acetaminophen exposure. The decision tree model had an accuracy of 0.839, an accuracy of 0.836, a recall of 0.72, a specificity of 0.86 and an F1_score of 0.76 for the test group and an accuracy of 0.848, a recall of 0.85, a recall of 0.74, a specificity of 0.87 and an F1_score of 0.78 for the training group. Our results showed that elevated serum levels of liver enzymes, other liver function test abnormality, anorexia, acidosis, electrolyte abnormality, increased bilirubin, coagulopathy, abdominal pain, coma, increased anion gap, tachycardia and hypotension were the most important factors in determining the outcome of acute acetaminophen exposure. Therefore, the decision tree model is a reliable approach in determining the prognosis of acetaminophen exposure cases and can be used in an emergency room or during hospitalization. [ABSTRACT FROM AUTHOR]

The risk for sexual victimization in college is high, and although there can be significant mental and physical consequences for the survivor, resource utilization for this issue is extremely low. The current study used the frameworks of both the ecological model and the theory of planned behavior to examine factors related to resource use in undergraduate survivors of unwanted sexual experiences. The sample consisted of 217 undergraduates who reported at least one unwanted sexual experience while in college (89% female, 76.5% Caucasian, M age 19.6). Data were collected in an online survey from a private liberal arts college, and were analyzed using decision tree modeling. Results suggested that resource use was less related to demographic or event characteristics, but rather related to victim response to the event as well as perceptions about control over potential resource use. In addition to key assault prevention efforts, it is thus important to also support survivor mental health and self-efficacy postassault. [ABSTRACT FROM AUTHOR]

Objectives: To analyze the predictors of health care utilization among respondents to the National Agricultural Worker Survey. Specifically, we hypothesized that English proficiency would predict utilization of health care services within the last 2 years. Methods: Using the 2015–2016 National Agricultural Worker Survey, we performed a secondary data analysis to analyze the predictors of health care utilization within the last 2 years in the United States' agricultural worker population. Data were cleaned and analyzed using decision tree analysis, which produced a classification tree model that was trained on 90% of the data and validated on 10%. Results: Exposure to English was not a predictor of health care utilization in our classification tree. The first major partition that predicted utilization was insurance status. Additional partitions were on age, gender, hypertension diagnosis, and public aid. Conclusions: By partitioning on insurance status and use of public aid, the decision tree provided evidence that systemic factors are key determinants of health care utilization in the agricultural worker community. This highlights the importance of agencies that connect agricultural workers with resources that provide insurance and improve access to health care. This is especially important given that agricultural workers are one of the highest risk groups for occupational injury or death in the United States. [ABSTRACT FROM AUTHOR]

Supplemental digital content is available in the text. Background: About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. Objectives: The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes). Methods: This study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit HHC agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation). Results: Best performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/communication, and service use being the most frequent categories. Discussion: This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources. [ABSTRACT FROM AUTHOR]

Feedback to evaluees in forensic mental health assessment (FMHA) is an understudied and underdiscussed topic in the professional literature. In other psychological disciplines, feedback is a traditional part of the psychological assessment process, though there are potential reasons that feedback in FMHA might or should be treated differently. Because feedback will often be a matter of evaluator discretion, we determined that reviewing relevant factors for carefully considered practice could be useful to the forensic evaluator community. Thus, based on the results of three focus groups with forensic evaluators, this article offers guidance and a decision tree to help guide decision making about feedback in FMHA. Although feedback will not be appropriate in every situation, thoughtful consideration of the issue—and providing feedback when appropriate—is one way of respecting the rights and dignity of forensic evaluees. Public Significance Statement: This article provides concrete considerations for forensic evaluators when considering whether to provide feedback to evaluees. It offers factors to guide idiographic decision making about feedback while highlighting considerations unique to forensic mental health assessment. [ABSTRACT FROM AUTHOR]

Seasonal snow-derived water is a critical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow-water-equivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water management efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging publicly available data sources and open-source software. The model demonstrates the application of a limited feature space in a relatively simple ML architecture without the need for process-based formulations to effectively estimate spatially continuous SWE at a daily temporal resolution. Beginning with in situ SWE measurements (i.e., SNOTEL), lidar-derived terrain features, and temporal variables, we employ localized feature engineering and optimization via gradient-boosting decision trees to identify regionally unique drivers of snowpack dynamics and use the optimal features to train regionally independent artificial neural networks to estimate regional SWE at a 1 km spatial resolution. The model results yield respectable skill in reconstructed 1 km gridded SWE magnitudes in a hindcast simulation of the 2019 water year that is independent of the training and testing data. Comparing model estimates to over 6200 observations, the model demonstrates a weighted RMSE of 15.4 cm, Kling-Gupta Efficiency metric of 0.86, and a percent bias of 0.71% across 23 snow-influenced regions in the western U.S. The model simulation produces peak SWE estimates within 10 cm for twenty of the twenty-three regions, demonstrating capability in effectively capturing regional snow accumulation processes. The demonstration of low-error ML workflows capable of providing near-real-time, spatially continuous SWE estimates at a high spatial resolution provides proof-of-concept and a foundation to effectively update snow state variables that drive water supply forecasts in snow-dominated regions. • A new Machine Learning-based SWE estimation model for the Western United States. • Utilization of open-source tools and up-to-date in-situ observations to estimate SWE. • Reconstructed SWE magnitudes exhibit respectable skill in a hindcast simulation. • Output may serve as a supplemental state-of-snowpack input to hydrologic models. [ABSTRACT FROM AUTHOR]

• Patients prefer at-home drug disposal and conventional takeback programs equally. • Product cost is the most salient determinant of patient preference. • Home-use products distributed by pharmacists are most preferred. • Findings support planned US FDA risk evaluation and mitigation policy. Unused opioid medication in the home increases risk of medication diversion, misuse, and unintended harm. The United States Federal Food and Drug Administration is currently considering the implementation of a risk evaluation and mitigation strategy (REMS) program that would require US pharmacists to provide drug disposal products with opioid prescriptions. Still, little is known about consumer preference for drug disposal method. The objective of this study was to identify product and program characteristics associated with consumer preference for at-home drug disposal products. A 2x2x3x3 full-factorial design was employed to text-based vignettes representing opioid analgesic disposal scenarios. Each vignette varied on four characteristics: product cost (free vs paid), ease of use (a mail back envelope, bringing medication to a takeback site, and an at-home drug deactivation pouch), potential environmental impact (incineration), and point of access (pharmacy, community organization, and prescriber). Of the 36 possible vignettes, 12 were removed as they represented a non-realistic combination of vignette characteristics. The remaining 24 were administered to a panel of patients with controlled-substance use in the past six-months. Decision tree modeling and general linear mixed (GLM) models were used sequentially to identify product characteristics associated with patient drug preferences A total of 1,006 participants completed all vignette drug disposal scenarios. Regression tree analysis found that the most important predictor of use was cost followed by ease of access and product design. GLM showed that takeback programs offered at a pharmacy were the most preferred disposal option followed by at-home products (mailed envelope or deactivation system) dispensed with the prescription. Programs that provide disposal resources directly to the patient at no cost with their prescription are likely to optimize willingness to dispose. Findings support the FDA's plan for a REMS program requiring pharmacies to distribute mail-back envelopes to patients when dispensed opioids. [ABSTRACT FROM AUTHOR]

Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement: Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models. [ABSTRACT FROM AUTHOR]

Annual turnover of home care workers represents a huge loss of revenue and is a key source of inefficiency in the home health care industry. In this article, we propose a data-driven approach to monitor employee churn and to capture the evolution of employee intent to leave. Unlike most papers in the literature, we use machine learning techniques to analyze over 2 million visits in the US, Canada, and Australia between 2016 and 2019. Results show that the gap between the number of hours worked and in the contract is the most important factor to predict employee intent to leave, which means an employee should be given as many hours as requested in the contract to improve retention. Secondary results show that having diverse shift lengths and continuity in services and patients seem to be associated with less turnover. [ABSTRACT FROM AUTHOR]

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches. [ABSTRACT FROM AUTHOR]

Increasingly, medical writers are being asked to work with health economics and outcomes research (HEOR) information. HEOR uses data from both economic and clinical research to assess the clinical and economic value of new treatments. Many medical writers have the underlying skills required to incorporate HEOR concepts into their work but need more background to do this comfortably. This article provides an introduction to health economics study design, key terms, and commonly used outcomes research approaches. It also describes how health economics writing resembles and differs from traditional medical writing. It briefly explores the present and future role of HEOR in US health policy. [ABSTRACT FROM AUTHOR]

Aim Although obesity is an established risk factor for cancer mortality among women, little is known about how body mass index ( BMI) is interacting with certain lifestyle behaviors and sociodemographic characteristics to increase the risk of cancer mortality. The purpose of this study was to use classification trees to examine possible interactions between BMI, smoking, age, poverty level and marital status in identifying high-risk subgroups for all-cause cancer mortality in women. Material and Methods Data from the US National Health Interview Survey linked with the National Death Index from 1990-2004 were used in this study. The data were restricted to women with complete information on all risk factors considered in the analyses. The data were analyzed using descriptive statistics and classification trees. Results Findings revealed that younger women (41-50 years old) who smoked had higher cancer mortality if they were underweight. Even among 51-60-year-old never smokers, we observed an inverse relationship between BMI and mortality. High BMI, however, was not a protective factor in 51-60-year-old women who were not married and did not smoke. Conclusion Classification tree analysis confirms and extends current knowledge about the role of BMI and smoking on cancer mortality by providing preliminary risk profiles. The findings indicate that it is imperative for cancer-related studies to examine BMI and smoking within age-specific groups and in the context of several sociodemographic factors that are known to independently affect cancer mortality. [ABSTRACT FROM AUTHOR]

Supplemental digital content is available in the text. OBJECTIVE: The aim of this study was to describe the relationships between intent to leave, reasons nurses intend to leave, and the nursing work environment in military hospitals. BACKGROUND: Intention to leave is a precursor of nurse turnover. The reasons nurses intend to leave may be influenced by leader interventions and potentially preventable. METHODS: This descriptive, correlational secondary analysis included 724 nurse survey responses from 23 US Army hospitals. Bivariate correlations and predictive modeling techniques were used. RESULTS: Forty-nine percent of nurses indicated they intended to leave, 44% for potentially preventable reasons. Dissatisfaction with management and the nursing work environment were the top potentially preventable reasons to leave. Nurses who intended to leave for potentially preventable reasons scored aspects of the nursing work environment significantly lower than those intending to leave for nonpreventable reasons. CONCLUSIONS: Identifying potentially preventable reasons in conjunction with intent to leave can provide leaders opportunities to intervene and influence turnover intention. [ABSTRACT FROM AUTHOR]

Background: Minimal clinically important difference (MCID) scores for outcome measures are frequently used evidence-based guides to gage meaningful changes. There are numerous outcome instruments used for analyzing pain, disability, and dysfunction of the low back; perhaps the most common of these is the Oswestry disability index (ODI). A single agreed-upon MCID score for the ODI has yet to be established. What is also unknown is whether selected baseline variables will be universal predictors regardless of the MCID used for a particular outcome measure. Objective: To explore the relationship between predictive models and the MCID cutpoint on the ODI. Setting: Data were collected from 16 outpatient physical therapy clinics in 10 states. Design: Secondary database analysis using backward stepwise deletion logistic regression of data from a randomized controlled trial (RCT) to create prognostic clinical prediction rules (CPR). Participants and Interventions: One hundred and forty-nine patients with low back pain (LBP) were enrolled in the RCT. All were treated with manual therapy, with a majority also receiving spine-strengthening exercises. Results: The resultant predictive models were dependent upon the MCID used and baseline sample characteristics. All CPR were statistically significant (P < 0.01). All six MCID cutpoints used resulted in completely different significant predictor variables with no predictor significant across all models. Limitations: The primary limitations include sub-optimal sample size and study design. Conclusions: There is extreme variability among predictive models created using different MClDs on the ODI within the same patient population. Our findings highlight the instability of predictive modeling, as these models are significantly affected by population baseline characteristics along with the MCID used. Clinicians must be aware of the fragility of CPR prior to applying each in clinical practice. [ABSTRACT FROM AUTHOR]

The author describes the rationale for a policy that all research articles in journal would require evidence of Institutional Review Board (IRB) approval or exemption prior to publication. He highlights the importance of human subject research protection as per National Research Act of 1974, which includes informed consent; equitable selection of research subjects, and beneficence. He offers information on the types of studies/articles that will require IRB approval, especially case reports.

Precisely and efficiently identifying subgroups with heterogeneous treatment effects (HTEs) in real-world evidence studies remains a challenge. Based on the causal forest (CF) method, we developed an iterative CF (iCF) algorithm to identify HTEs in subgroups defined by important variables. Our method iteratively grows different depths of the CF with important effect modifiers, performs plurality votes to obtain decision trees (subgroup decisions) for a family of CFs with different depths, and then finds the cross-validated subgroup decision that best predicts the treatment effect as a final subgroup decision. We simulated 12 different scenarios and showed that the iCF outperformed other machine learning methods for interaction/subgroup identification in the majority of scenarios assessed. Using a 20% random sample of fee-for-service Medicare beneficiaries initiating sodium-glucose cotransporter-2 inhibitors or glucagon-like peptide-1 receptor agonists, we implemented the iCF to identify subgroups with HTEs for hospitalized heart failure. Consistent with previous studies suggesting patients with heart failure benefit more from sodium-glucose cotransporter-2 inhibitors, iCF successfully identified such a subpopulation with HTEs and additive interactions. The iCF is a promising method for identifying subgroups with HTEs in real-world data where the potential for unmeasured confounding can be limited by study design. [ABSTRACT FROM AUTHOR]

Abstract: Background: Congenital cytomegalovirus (CMV) infection is associated with significant infant morbidity and mortality. A prophylactic vaccine to prevent congenital CMV infection is expected to be available in the near future, and will likely be targeted to adolescent females. Methods: Using a decision tree, we compared the costs, potential clinical impacts, and cost-effectiveness of the current strategy of no CMV vaccination versus a strategy where all adolescent females are vaccinated against CMV prior to their first pregnancy. Both maternal outcomes related to vaccination, and infant outcomes related to congenital CMV infection, were considered in the model. Results: Under base-case conditions, our analysis suggested that vaccinating all adolescent females against cytomegalovirus would be both less costly and with greater clinical benefits than not vaccinating. Among a population of 100,000 adolescent females, the vaccination strategy cost $32.3 million dollars less than not vaccinating, and avoided substantial numbers of infants affected with hearing loss, vision loss, and mental retardation, and 8 infant deaths. Our model was most sensitive to variations in vaccine efficacy. When vaccine efficacy against disease was less than 61%, not vaccinating became the preferred strategy because it was less expensive than vaccinating, without substantial changes in clinical benefits to the population. Conclusions: Under a wide variety of conditions, universal vaccination of adolescent females to protect their future children against congenital CMV infection was cost effective. However, for this to be preferred over not vaccinating, our results suggest that vaccine efficacy against disease would need to be at least 61%. [Copyright &y& Elsevier]

Abstract: Objective: This study updated a 2001 decision economic model that used indirect data and confirmed its findings by developing a new cost-effectiveness model by using now available head-to-head data. The models compared olanzapine with ziprasidone in the treatment of schizophrenia in the United States. Methods: A decision analytic modeling approach was used to estimate annual health-care costs and health outcomes, incorporating events such as response, relapse, and suicide. Patients without response to first-line treatment switched to the other comparator. Decision tree probabilities were extracted from head-to-head studies and other published clinical literature. Direct health-care costs and quality-adjusted life-years (QALYs) were estimated on the basis of resource use and utility weights for initial and relapse episodes, maintenance therapy, and extended episodes of illness. Disutilities associated with treatment-emergent adverse events were included. Results: Consistent with the 2001 model, this model found that first-line treatment with olanzapine is associated with fewer hospital days, fewer days with extrapyramidal symptoms, and higher QALYs than is first-line treatment with ziprasidone. Total costs were lower for the olanzapine pathway ($70,232–$72,776 vs. $73,086–$73,310 in the Positive and Negative Syndrome Scale analysis) due to the cost savings associated with reduced health-care resource use. The incremental cost per QALY gained indicated that the olanzapine pathway dominated the ziprasidone pathway. Conclusions: Decision analytic models should be continuously assessed against new data. This case study shows that incorporating new data confirmed results of a previously published model in which olanzapine was associated with better expected health outcomes and lower total health-care costs than was ziprasidone. [Copyright &y& Elsevier]

The 1990 Americans with Disabilities Act has had a profound influence on social workers and social services administrators in virtually all work settings. Because of the multiple elements of the act, however, assessing the validity of claims can be a somewhat arduous and complicated task. This article provides a "decision tree" for administrators to assist with the evaluation of claims. This decision tree allows people who are considering the validity of an ADA claim to break the decision-making process into discrete steps that can be considered separately and sequentially. These steps include employee and disability status, employer knowledge of the disability, employee qualification for the job, the provision of accommodations, the adverse actions that may be included in a claim, valid employer rationales for adverse action, and the procedural elements required for a successful ADA claim. Issues that are important in each step are discussed. [ABSTRACT FROM AUTHOR]

This paper studies an uneven two-class unsupervised classification problem of satellite imagery, i.e., the mapping of U.S. Department of Agriculture's (USDA) Conservation Reserve Pro- gram (CRP) tracts. CRP is a nationwide program that encourages farmers to plant long-term, resource conserving coven to improve soil, water, and wildlife resources. With recent payments of nearly US $1.6 billion for new enrollments (2002 signup), it is imperative to obtain accurate digital CRP maps for management and evaluation purposes. CRP mapping is a complex classification problem where both CRP and non-CRP areas are composed of various cover types. Two nonparametric machine learning approaches, i.e., decision tree classifier (DTC) and support vector machine (SVM5) are implemented in this work. Specifically, considering the importance of CRP classification sensitivity, a new DTC pruning method is proposed to increase recall. We also study two SVM relaxation approaches to increase recall. Moreover, a localized and parallel framework is suggested in order to efficiently deal with the large-scale CRP mapping need. Simulation results validate the applicability of the suggested framework and proposed techniques. [ABSTRACT FROM AUTHOR]

Objective  American College of Cardiology and American Heart Association (ACC/AHA) published new guidelines which lower the cut-off for hypertension. We sought to evaluate the impact of these guidelines to cost and benefit of various low-dose aspirin prophylaxis approaches. Study Design  Decision tree analysis was created using R software to evaluate four approaches to aspirin prophylaxis in the United States: no aspirin, United States Preventive Service Task Force (USPSTF) with Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7) hypertension guidelines, USPSTF with ACC/AHA hypertension guidelines, as well as universal aspirin prophylaxis. This model was executed to simulate a hypothetical cohort of 4 million pregnant women in the United States. Results  The new guidelines would expand the aspirin eligibility by 8% (76,953 women) in the USPSTF guidelines. Even with this increased eligibility, the USPSTF guidelines continue to be the approach with the most cost savings ($386.5 million) when compared with universal aspirin and no aspirin prophylaxis. The new hypertension guidelines are projected to increase the cost savings of the USPSTF approach by $9.4 million. Conclusion  Despite the small change in aspirin prophylaxis, using ACC/AHA definition of hypertension still results in an annual cost-saving of $9.4 million in the United States when compared with JNC7. [ABSTRACT FROM AUTHOR]

Quantitative sociologists and social policymakers are increasingly interested in local context. Some city-specific studies have developed new primary data collection efforts to analyze inequality at the neighborhood level, but methods from spatial microsimulation have yet to be broadly used in sociology to take better advantage of existing public data sets. The American Community Survey (ACS) is the largest household survey in the United States and indispensable for detailed analysis of specific places and populations. The authors propose a technique, tree-based spatial microsimulation, to produce "small-area" (census-tract) estimates of any person- or household-level phenomenon that can be derived from ACS microdata variables. The approach is straightforward and computationally efficient, based only on publicly available data, and it provides more reliable estimates than do prevailing methods of microsimulation. The authors demonstrate the technique's capabilities by producing tract-level estimates, stratified by race/ethnicity, of (1) the proportion of people in the census-tract population who have children and work in an essential occupation and (2) the proportion of people in the census-tract population living below the federal poverty threshold and in a household that spends greater than 50 percent of monthly income on rent or owner costs. These examples are relevant to understanding the sociospatial inequalities dramatized by the coronavirus disease 2019 pandemic. The authors discuss potential extensions of the technique to derive small-area estimates of variables observed in surveys other than the ACS. [ABSTRACT FROM AUTHOR]

Background: Colonoscopy is the most widely used colorectal cancer (CRC) screening test in the United States. Through the detection and removal of potentially precancerous polyps, it can prevent CRC. However, CRC screening remains low among adults who are recommended for screening. The New Hampshire Colorectal Cancer Screening Program implemented a patient navigation (PN) intervention to increase colonoscopy screening among low-income patients in health centers in New Hampshire. In the current study, the authors examined the cost-effectiveness of this intervention.Methods: A decision tree model was constructed using Markov state transitions to calculate the costs and effectiveness associated with PN. Costs were calculated for the implementation of PN in a statewide public health program and in endoscopy centers. The main study outcome was colonoscopy screening completion. The main decision variable was the incremental cost-effectiveness ratio associated with the PN intervention compared with usual care.Results: The average cost per screening with PN was $1089 (95% confidence interval, $1075-$1103) compared with $894 with usual care (95% confidence interval, $886-$908). Among patients who were navigated, approximately 96.2% completed colonoscopy screening compared with 69.3% of those receiving usual care (odds ratio, 11.2; P <. 001). The incremental cost-effectiveness ratio indicated that 1 additional screening completion cost approximately $725 in a public health program and $548 in an endoscopy center with PN compared with usual care, both of which are less than the average Medicare reimbursement of $737 for a colonoscopy procedure.Conclusions: PN was found to be cost-effective in increasing colonoscopy screening among low-income adults in the New Hampshire Colorectal Cancer Screening Program, even at the threshold of current Medicare reimbursement rates for colonoscopy. The results of the current study support the implementation of PN in statewide public health programs and endoscopy centers. [ABSTRACT FROM AUTHOR]

Approximately seven years of daily initializations from the convection-allowing National Severe Storms Laboratory Weather Research and Forecasting Model are used as inputs to train random forest (RF) machine learning models to probabilistically predict instances of excessive rainfall. Unlike other hazards, excessive rainfall does not have an accepted definition, so multiple definitions of excessive rainfall and flash flooding—including flash flood reports and 24-h average recurrence intervals (ARIs)—are used to explore RF configuration forecast sensitivities. RF forecasts are analogous to operational Weather Prediction Center (WPC) day-1 Excessive Rainfall Outlooks (EROs) and their resolution, reliability, and skill are strongly influenced by rainfall definitions and how inputs are assembled for training. Models trained with 1-yr ARI exceedances defined by the Stage-IV (ST4) precipitation analysis perform poorly in the northern Great Plains and Southwest United States, in part due to a high bias in the number of training events in these regions. Increasing the ARI threshold to 2 years or removing ST4 data from training, optimizing forecast skill geographically, and spatially averaging meteorological inputs for training generally results in improved CONUS-wide RF forecast skill. Both EROs and RF forecasts have seasonal skill—–poor forecasts in the late fall and winter and skillful forecasts in the summer and early fall. However, the EROs are consistently and significantly better than their RF counterparts, regardless of RF configuration, particularly in the summer months. The results suggest careful consideration should be made when developing ML-based probabilistic precipitation forecasts with convection-allowing model inputs, and further development is necessary to consider these forecast products for operational implementation. Significance Statement: Machine learning (ML) models can deduce statistical relationships between a set of predictors and meteorological events. In this work, ML models are developed to predict excessive rainfall events. Since excessive rainfall is difficult to uniformly define across the United States, multiple ML models are built from a variety of rainfall datasets with predictors gathered from output of a high-resolution numerical weather prediction model and forecasts are made from each model. Forecasts made from these models are highly sensitive to both the definitions of excessive rainfall (e.g., 100 mm of rain in a day may cause flooding in a usually dry area, but not in a wet area) and the predictors used. Forecast skill can increase when excessive rainfall events are rarer and when predictors synthesize the surrounding environment rather than characterize specific geographical points. ML-based models have great potential for excessive rainfall prediction, but careful attention to the configuration of these models is required. [ABSTRACT FROM AUTHOR]

Background: Pressure ulcers are associated with a nearly three-fold increase in in-hospital mortality. It is essential to investigate how other factors besides the Braden scale could enhance the prediction of pressure ulcers. Data mining modeling techniques can be beneficial to conduct this type of analysis. Data mining techniques have been applied extensively in health care, but are not widely used in nursing research. Purpose: To remedy this methodological gap, this paper will review, explain, and compare several data mining models to examine patient level factors associated with pressure ulcers based on a four year study from military hospitals in the United States. Methods: The variables included in the analysis are easily accessible demographic information and medical measurements. Logistic regression, decision trees, random forests, and multivariate adaptive regression splines were compared based on their performance and interpretability. Results: The random forests model had the highest accuracy (C-statistic) with the following variables, in order of importance, ranked highest in predicting pressure ulcers: days in the hospital, serum albumin, age, blood urea nitrogen, and total Braden score. Conclusion: Data mining, particularly, random forests are useful in predictive modeling. It is important for hospitals and health care systems to use their own data over time for pressure ulcer risk prediction, to develop risk models based upon more than the total Braden score, and specific to their patient population. [ABSTRACT FROM AUTHOR]

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. [ABSTRACT FROM AUTHOR]

Background: Periprosthetic joint infection (PJI) following total knee arthroplasty is a growing concern, as the demand for total knee arthroplasty (TKA) expands annually. Although 2-stage revision is considered the gold standard in management, there is substantial morbidity and mortality associated with this strategy. One-stage revision is associated with lower mortality rates and better quality of life, and there has been increased interest in utilizing the 1-stage strategy. However, surgeons are faced with a difficult decision regarding which strategy to use to treat these infections, considering uncertainty with respect to eradication of infection, quality of life, and societal costs with each strategy. The purpose of the current study was to use decision analysis to determine the optimal decision for the management of PJI following TKA.Methods: An expected-value decision tree was constructed to estimate the quality-adjusted life-years (QALYs) and costs associated with 1-stage and 2-stage revision. Two decision trees were created: Decision Tree 1 was constructed for all pathogens, and Decision Tree 2 was constructed solely for difficult-to-treat infections, including methicillin-resistant infections. Values for parameters in the decision model, such as mortality rate, reinfection rate, and need for additional surgeries, were derived from the literature. Medical costs were derived from Medicare data. Sensitivity analysis determined which parameters in the decision model had the most influence on the optimal strategy.Results: In both decision trees, the 1-stage strategy produced greater health utility while also being more cost-effective. In the Monte Carlo simulation for Decision Trees 1 and 2, 1-stage was the dominant strategy in about 85% and 69% of the trials, respectively. Sensitivity analysis showed that the reinfection and 1-year mortality rates were the most sensitive parameters influencing the optimal decision.Conclusions: Despite 2-stage revision being considered the current gold standard for infection eradication in patients with PJI following TKA, the optimal decision that produced the highest quality of life was 1-stage revision. These results should be considered in shared decision-making with patients who experience PJI following TKA.Level Of Evidence: Economic and Decision Analysis Level IV. See Instructions for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

Background: Prolonged postoperative ileus (POI) is the predominant cause of extended hospitalization after bowel resection surgery. Alvimopan accelerates gastrointestinal recovery, potentially reducing health care costs. We examined the value of alvimopan in reducing prolonged POI and length of stay for patients undergoing abdominal surgery using different definitions of POI.Study Design: We developed a decision analytic model to examine costs and outcomes associated with postoperative treatment with either an accelerated care pathway (ACP) only or alvimopan+ACP. To represent an overall perspective for alvimopan, data from four phase 3 bowel resection trials and one phase 4 radical cystectomy trial were used to populate the model with 3 different definitions of POI. The period analyzed included start of surgery to 7 days post discharge. Costs were obtained from standard US costing sources and are reported in 2015 US dollars. Due to variations in published definitions of POI, alternative definitions based on adverse event reports, NG tube insertion, and time to food toleration were examined.Results: The combined clinical trial data included 1,003 ACP and 1,013 alvimopan+ACP patients. When POI was reported as an adverse event, the incidence of POI was significantly lower with alvimopan+ACP (n = 70 [7%]) vs ACP alone (n = 148 [15%]; p < 0.0001). Time to discharge order written was shorter for patients with POI who were treated with alvimopan+ACP than with ACP (202 ± 115 hours vs 266 ± 138 hours; p < 0.0001). As a result, costs were $731 lower with alvimopan+ACP ($17,835) vs ACP ($18,566). Alternative definitions of POI produced similar results.Conclusions: The addition of alvimopan to existing treatment pathways for patients undergoing abdominal surgery can reduce overall hospital costs. [ABSTRACT FROM AUTHOR]

Economic growth, air pollution, and forest fires in some states in the United States have increased the concentration of particulate matter with a diameter less than or equal to 2.5 μm (PM2.5). Although previous studies have tried to observe PM2.5 both spatially and temporally using aerosol remote sensing and geostatistical estimation, they were limited in accuracy by coarse resolution. In this paper, the performance of machine learning models on predicting PM2.5 is assessed with linear regression (LR), decision tree (DT), gradient boosting regression (GBR), AdaBoost regression (ABR), XGBoost (XGB), k‐nearest neighbors (K‐NN), long short‐term memory (LSTM), random forest (RF), and support vector machine (SVM) using PM2.5 station data from 2017 to 2021. To compare the accuracy of all the nine machine learning models, the coefficient of determination (R2), root mean square error (RMSE), Nash‐Sutcliffe efficiency (NSE), root mean square error ratio (RSR), and percent bias (PBIAS) were evaluated. Among all nine models, the RF (100 decision trees with a max depth of 20) and support vector regression (SVR; nonlinear kernel, degree 3 polynomial) models were the best for predicting PM2.5 concentrations. Additionally, comparison of the PM2.5 performance metrics displayed that the models had better predictive behavior in the western United States than that in the eastern United States. Key Points: We present the prediction of PM2.5 concentrations over the United States using various machine learning (ML) algorithmsWe show ML as a new approach for analyzing large data sets due to the computational speed and easy implementation for massive amounts of dataThe study is important for improving our understanding of the differences among ML algorithms for Earth Science research [ABSTRACT FROM AUTHOR]

Recent developments in healthcare reform legislation and in the private-payer marketplace have increased impetus toward clinical integration. Industry changes require that healthcare delivery institutions confront fundamental scope and scale structural issues that may lead to increased vertical integration. To accomplish integration, firms must decide the organizational form of integration (alliance or merger/acquisition). One form of integration, accountable care organizations (ACOs), has featured prominently in recent legislation. Clinical integration and ACOs present significant shared-governance challenges that must be understood by hospital boards. The author outlines these governance issues using a case study of Silver Cross Hospital's governance structure for its ACO. [ABSTRACT FROM AUTHOR]

A new complex quality assurance (QA) procedure is developed for historical hourly surface airways meteorological data, recently digitized in a U.S. government–sponsored effort that extends the digital period of record back as early as the late 1920s. The procedure builds upon an existing, three-pronged framework and incorporates several checks previously employed for hourly data. New or enhanced checks were also developed, partly with the goal of accommodating the peculiarities of historical observation practices or reporting standards, but also to improve our ability to flag suspicious data. The latter goal is sought by use of more elaborate physics or empirical relationships than are employed in existing checks, or by modifying or replacing them to sharpen their intended focus. Each of the three prongs checks for consistency, whether for internal or temporal consistency or against limits. Six internal consistency and three temporal consistency checks are developed or adapted from recent research and implemented as components in complex QA. These involve temperature, humidity, pressure, wind, present weather, and cloud cover from synoptic reports, as well as station elevation from metadata. In order to determine, from the many component-check flags activated, which element(s) are most likely responsible for the flag(s), a decision tree was developed. Resultant flag levels “suspect” and “erroneous” reflect the degree of confidence. Flag rates averaged 9.5% overall but leveled off at about 2% after 1937 (0.3% for “erroneous” flags), comparing favorably with previously published flag rates. Systematic errors raise many “suspect” flags, suggesting that hybrid QA–inhomogeneity tests are needed. [ABSTRACT FROM AUTHOR]

This paper presents a Stormwater Emergency Response Framework (SERF) for use in the containment and treatment of stormwater runoff following a hazardous material release. The framework consists of four high level process steps and a decision tree. These resources are intended to assist stormwater managers in fulfilling their emergency response responsibilities within the United States' National Incident Management System. Robust hydraulic and watershed modeling may take weeks to months to develop for a contaminated site, whereas decisions made in the initial hours can have a significant impact on limiting contamination spread. Many web resources are publicly available to assist responders in visualizing stormwater runoff flow paths. A case study provided in this paper also demonstrates how simple calculations may be utilized to estimate peak flows and storage volumes necessary to respond to precipitation events immediately. These calculations are useful for decision makers' allocation of containment and treatment resources within the impacted area. This includes where to deploy available resources to minimize contamination risks to downstream communities and where supplemental resources from outside partners are urgently needed. • Process steps and decision tree for response to a hazardous material release. • Overview of web resources available to rapidly visualize runoff flow paths. • Framework application to a notional wide-area contamination case study. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

The clinical utility of the 70-gene signature (MammaPrint®) to guide chemotherapy use in T1-3N0-1M0 breast cancer was demonstrated in the Microarray in Node-Negative and 1 to 3 Positive Lymph Node Disease May Avoid Chemotherapy (MINDACT) study. One thousand four ninety seven of 3356 (46.2%) enrolled patients with high clinical risk (in accordance with the modified Adjuvant! Online clinical-pathological assessment) had a low-risk 70-gene signature. Using patient-level data from the MINDACT trial, the cost-effectiveness of using the 70-gene signature to guide adjuvant chemotherapy selection for clinical high risk, estrogen receptor positive (ER+), human epidermal growth factor 2 negative (HER2-) patients was analysed. A hybrid decision tree-Markov model simulated treatment strategies in accordance with the 70-gene signature with clinical assessment versus clinical assessment alone, over a 10-year time horizon. Primary outcomes were quality-adjusted life years (QALYs), country-specific costs and incremental cost-effectiveness ratios (ICERs) for six countries: Belgium, France, Germany, Netherlands, UK and the US. Treatment strategies guided by the 70-gene signature result in more QALYs compared with clinical assessment alone. Costs of the 70-gene signature strategy were lower in five of six countries. This led to dominance of the 70-gene signature in Belgium, France, Germany, Netherlands and the US and to a cost-effective situation in the UK (ICER £22,910/QALY). Annual national cost savings were €4.2M (Belgium), €24.7M (France), €45.1M (Germany), €12.7M (Netherlands) and $244M (US). UK budget increase was £8.4M. Using the 70-gene signature to safely guide chemotherapy de-escalation in clinical high risk patients with ER+/HER2- tumours is cost-effective compared with using clinical assessment alone. Long-term follow-up and outcomes from the MINDACT trial are necessary to address uncertainties in model inputs. • The 70-gene signature is cost-effective compared with clinical assessment alone • Minimal survival differences are balanced against quality of life gains • Results are consistent amongst the five studied countries [ABSTRACT FROM AUTHOR]