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Inconsistencies between performance data from laboratory‐prepared and field samples have been widely reported. These inconsistencies often result in inaccurate condition prediction, which leads to inefficient maintenance planning. Traditional pavement management systems (PMS) do not have the appropriate means (e.g., mechanistic solutions, extensive data handling facilities, etc.) to consider these data inconsistencies. With the growing demand for sustainable materials, there is a need for more self‐learning systems that could quickly transfer laboratory‐based information to field‐based information inside the PMS. The article aims to present a future‐ready machine learning‐based framework for analyzing the differences between laboratory and field‐prepared samples. Developed on the basis of data obtained from field and laboratory data, the gradient‐boosting decision trees‐based framework was able to establish a good relationship between laboratory performance and field performance (R2test > 80 for all models). At the same time, the framework could also show more complex relationships that are often not considered in practice. [ABSTRACT FROM AUTHOR]

The mixture cure model is widely used to analyze survival data in the presence of a cured subgroup. Standard logistic regression‐based approaches to model the incidence may lead to poor predictive accuracy of cure, specifically when the covariate effect is non‐linear. Supervised machine learning techniques can be used as a better classifier than the logistic regression due to their ability to capture non‐linear patterns in the data. However, the problem of interpret‐ability hangs in the balance due to the trade‐off between interpret‐ability and predictive accuracy. We propose a new mixture cure model where the incidence part is modeled using a decision tree‐based classifier and the proportional hazards structure for the latency part is preserved. The proposed model is very easy to interpret, closely mimics the human decision‐making process, and provides flexibility to gauge both linear and non‐linear covariate effects. For the estimation of model parameters, we develop an expectation maximization algorithm. A detailed simulation study shows that the proposed model outperforms the logistic regression‐based and spline regression‐based mixture cure models, both in terms of model fitting and evaluating predictive accuracy. An illustrative example with data from a leukemia study is presented to further support our conclusion. [ABSTRACT FROM AUTHOR]

Global declines in ecosystem extent and condition mean there is an increasing demand for recovery and conservation plans. Conservation plans for ecological communities require a management framework with measurable, time‐bound objectives. Efficient and structured processes that facilitate timely and comparable conservation plans are essential, especially where resources are constrained. We describe a process to streamline the development of conservation plans by combining functionally similar community sub‐types into a multi‐community State and Transition Model that can be used to guide conservation planning. We demonstrate this approach in a case study using eucalypt dominated woodlands of southern Australia—an ecosystem which occupies a vast geographical range across temperate Australia and includes many distinct vegetation communities, a growing number of which are endangered or threatened. Australian woodland ecologists (grouped according to their knowledge of three broad woodland sub‐types) were asked to develop causal chains to describe all factors associated with transitions among woodland condition states, and estimate the likelihoods associated with each transition at two time‐scales. The resultant State and Transition Model includes a set of eight general condition states that are common to eucalypt dominated woodlands, and 364 unique causal chains describing the drivers of all plausible transitions. We include an example of how the same information can be presented as a series of decision trees aimed at supporting on‐ground management decisions. The case study demonstrates that it is possible to construct a detailed State and Transition Model that synthesizes knowledge across multiple similar vegetation communities. To date, State and Transition Models focused on single communities or a smaller spatial scale, and this is the first attempt to construct a nationally relevant multi‐community State and Transition Model via a structured and participatory process. Synthesis and applications. This approach can be applied at multiple spatial scales to improve and streamline the development of robust conservation plans to improve how we plan for, implement and measure global biodiversity outcomes. [ABSTRACT FROM AUTHOR]

As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model‐agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier‐to‐explain. Yet, rule sets can become very lengthy, with many if–else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behaviour of complex ML models by providing surrogate decision stumps (one‐level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity versus fidelity trade‐off by incrementally generating more stumps, creating attribute‐based explanations with weighted stumps to justify decision making, and analysing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case‐by‐case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers. [ABSTRACT FROM AUTHOR]

At present, data obtained from the Global Positioning System (GPS) is significantly valuable in mobility research. However, GPS‐based data lacks include trip purpose information. Consequently, many researchers have endeavoured to predict or impute these missing attributes. Existing studies have focused on constructing more features to improve prediction accuracy, but paid less attention to the model's applicability and transferability. In this study, five trip purposes are extracted, including education, recreation, personal, shopping, and transportation, from Chengdu Household Travel Survey (HTS) data. The individual and trip characteristics that are common and can be easily derived from GPS data are carefully selected and extracted. Point of Interest (POI) data of the trip destination are also collected to enhance input characteristics. To obtain more accurate results, an ensemble learning model, Gradient Boosting Decision Trees (GBDT), is employed to predict trip purposes. grid search and cross‐validation techniques are used to optimize the hyper‐parameters. Empirical results show that the proposed model achieves 0.788 accuracy, which is 22.17%, 14.53%, 10.36%, and 6.77% higher than Multinominal Logit (MNL), Artificial Neural Network (ANN), Random Forest (RF), and Deep Belief Network (DBN), respectively. It is also found that although increasing trip features improve the model's accuracy, it simultaneously impairs model's transferability and generalizability. [ABSTRACT FROM AUTHOR]

Statistical methods generating individualized treatment rules (ITRs) often focus on maximizing expected benefit, but these rules may expose patients to excess risk. For instance, aggressive treatment of type 2 diabetes (T2D) with insulin therapies may result in an ITR which controls blood glucose levels but increases rates of hypoglycemia, diminishing the appeal of the ITR. This work proposes two methods to identify risk‐controlled ITRs (rcITR), a class of ITR which maximizes a benefit while controlling risk at a prespecified threshold. A novel penalized recursive partitioning algorithm is developed which optimizes an unconstrained, penalized value function. The final rule is a risk‐controlled decision tree (rcDT) that is easily interpretable. A natural extension of the rcDT model, risk controlled random forests (rcRF), is also proposed. Simulation studies demonstrate the robustness of rcRF modeling. Three variable importance measures are proposed to further guide clinical decision‐making. Both rcDT and rcRF procedures can be applied to data from randomized controlled trials or observational studies. An extensive simulation study interrogates the performance of the proposed methods. A data analysis of the DURABLE diabetes trial in which two therapeutics were compared is additionally presented. An R package implements the proposed methods (https://github.com/kdoub5ha/rcITR). [ABSTRACT FROM AUTHOR]

This study combines the use of multi‐objective decision trees (MODT) and a fuzzy TOPSIS approach to cope with sequential decision‐making under uncertainty with multiple conflicting objectives. To this aim, the MODT generates a Pareto‐optimal frontier of solutions for competing objectives, while fuzzy TOPSIS helps assess the noninferior solutions and assign weights to the objectives by considering possible uncertainties in outcomes as well as potential linguistic expression. The proposed approach is illustrated with a temporal decision problem concerning climate‐driven refugee resettlement, taking into consideration the challenges of global warming and system interdependencies in an uncertain context. The fuzzy TOPSIS approach was illustrated using two different procedures, and the procedure based on vector normalisation graphically depicts the fuzzy triangular representation of the final ranking. [ABSTRACT FROM AUTHOR]

Conducting high‐quality overviews of reviews (OoR) is time‐consuming. Because the quality of systematic reviews (SRs) varies, it is necessary to critically appraise SRs when conducting an OoR. A well‐established appraisal tool is A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2, which takes about 15–32 min per application. To save time, we developed two fast‐and‐frugal decision trees (FFTs) for assessing the methodological quality of SR for OoR either during the full‐text screening stage (Screening FFT) or to the resulting pool of SRs (Rapid Appraisal FFT). To build a data set for developing the FFT, we identified published AMSTAR 2 appraisals. Overall confidence ratings of the AMSTAR 2 were used as a criterion and the 16 items as cues. One thousand five hundred and nineteen appraisals were obtained from 24 publications and divided into training and test data sets. The resulting Screening FFT consists of three items and correctly identifies all non‐critically low‐quality SRs (sensitivity of 100%), but has a positive predictive value of 59%. The three‐item Rapid Appraisal FFT correctly identifies 80% of the high‐quality SRs and correctly identifies 97% of the low‐quality SRs, resulting in an accuracy of 95%. The FFTs require about 10% of the 16 AMSTAR 2 items. The Screening FFT may be applied during full‐text screening to exclude SRs with critically low quality. The Rapid Appraisal FFT may be applied to the final SR pool to identify SR that might be of high methodological quality. [ABSTRACT FROM AUTHOR]

The exponential growth of digital text documents presents a significant challenge for text classification algorithms, as the vast number of words in each document can hinder their efficiency. Feature selection (FS) is a crucial technique that aims to eliminate irrelevant features and enhance classification accuracy. In this study, we propose an improved version of the discrete laying chicken algorithm (IDLCA) that utilizes noun‐based filtering to reduce the number of features and improve text classification performance. Although LCA is a newly proposed algorithm, it has not been systematically applied to discrete problems before. Our enhanced version of LCA employs different operators to improve both exploration and exploitation of this algorithm to find better solutions in discrete mode. To evaluate the effectiveness of the proposed method, we compared it with some conventional nature‐inspired feature selection methods using various learning models such as decision trees (DT), K‐nearest neighbor (KNN), Naive Bayes (NB), and support vector machine (SVM) on five benchmark datasets with three different evaluation metrics. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison to the existing one. The code is available at https://github.com/m0javad/Improved-Discrete-Laying-Chicken-Algorithm. [ABSTRACT FROM AUTHOR]

Introduction: Clinical practice guidelines (hereafter 'guidelines') are crucial in providing evidence‐based recommendations for physicians and multidisciplinary teams to make informed decisions regarding diagnostics and treatment in various diseases, including cancer. While guideline implementation has been shown to reduce (unwanted) variability and improve outcome of care, monitoring of adherence to guidelines remains challenging. Real‐world data collected from cancer registries can provide a continuous source for monitoring adherence levels. In this work, we describe a novel structured approach to guideline evaluation using real‐world data that enables continuous monitoring. This method was applied to endometrial cancer patients in the Netherlands and implemented through a prototype web‐based dashboard that enables interactive usage and supports various analyses. Method: The guideline under study was parsed into clinical decision trees (CDTs) and an information standard was drawn up. A dataset from the Netherlands Cancer Registry (NCR) was used and data items from both instruments were mapped. By comparing guideline recommendations with real‐world data an adherence classification was determined. The developed prototype can be used to identify and prioritize potential topics for guideline updates. Results: CDTs revealed 68 data items for recording in an information standard. Thirty‐two data items from the NCR were mapped onto information standard data items. Four CDTs could sufficiently be populated with NCR data. Conclusion: The developed methodology can evaluate a guideline to identify potential improvements in recommendations and the success of the implementation strategy. In addition, it is able to identify patient and disease characteristics that influence decision‐making in clinical practice. The method supports a cyclical process of developing, implementing and evaluating guidelines and can be scaled to other diseases and settings. It contributes to a learning healthcare cycle that integrates real‐world data with external knowledge. [ABSTRACT FROM AUTHOR]

Few studies included objective blood pressure (BP) to construct the predictive model of severe obstructive sleep apnea (OSA). This study used binary logistic regression model (BLRM) and the decision tree method (DTM) to constructed the predictive models for identifying severe OSA, and to compare the prediction capability between the two methods. Totally 499 adult patients with severe OSA and 1421 non‐severe OSA controls examined at the Sleep Medicine Center of a tertiary hospital in southern Taiwan between October 2016 and April 2019 were enrolled. OSA was diagnosed through polysomnography. Data on BP, demographic characteristics, anthropometric measurements, comorbidity histories, and sleep questionnaires were collected. BLRM and DTM were separately applied to identify predictors of severe OSA. The performance of risk scores was assessed by area under the receiver operating characteristic curves (AUCs). In BLRM, body mass index (BMI) ≥27 kg/m2, and Snore Outcomes Survey score ≤55 were significant predictors of severe OSA (AUC 0.623). In DTM, mean SpO2 <96%, average systolic BP ≥135 mmHg, and BMI ≥39 kg/m2 were observed to effectively differentiate cases of severe OSA (AUC 0.718). The AUC for the predictive models produced by the DTM was higher in older adults than in younger adults (0.807 vs. 0.723) mainly due to differences in clinical predictive features. In conclusion, DTM, using a different set of predictors, seems more effective in identifying severe OSA than BLRM. Differences in predictors ascertained demonstrated the necessity for separately constructing predictive models for younger and older adults. [ABSTRACT FROM AUTHOR]

Motivation: Industrial policy is on the government agenda in many countries at different stages of development. Despite an extensive academic literature, much of the discussion is too general to guide policy‐makers. Purpose: The article reviews the taxonomies of industrial policy in the literature, highlights some of their limitations and offers a simple alternative based on constraints to industrial development. It applies this framework in the context of one low‐income country —Ethiopia—and suggests a problem‐tree approach to address the binding constraints to growth in particular sectors. Methods: Case studies of Ethiopia's garment, leather goods and cut‐flowers sectors are used as illustrations of the simple taxonomy proposed. Conclusion: In low‐income countries, the key issues for industrial policy often relate to basic issues, such as the provision of adequate infrastructure and access to land, rather than to the choice of particular forms of incentive. Policy Implications: By focusing on different markets, the simple taxonomy proposed could clarify government options in deciding how best to overcome critical constraints to competitiveness in a given sector. [ABSTRACT FROM AUTHOR]

Background: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS. Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC). Results: The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%–89.09% in training group, and 82.16%–88.74% in test group) and discriminatory power (AUC: 0.806–0.894 in training group, and 0.808–0.882 in test group) in both training and testing datasets. An interactive web‐based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma%5fsurvival%5fprediction/) was developed, simplifying the survival prediction process for clinicians. Conclusions: This study successfully employed RPA to develop a user‐friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision‐making. Future prospective studies are recommended to validate these findings and further refine the predictive model. [ABSTRACT FROM AUTHOR]

A variety of novel behavioral modeling techniques have been reported to accurately capture the nonlinear characteristics of GaN devices. For the purpose of describing GaN HEMT large‐signal behavior, machine learning‐based approaches have been proposed. There is, however, a lack of comparison and analysis of their performance with different machine learning techniques in these studies. An examination of machine learning‐based large‐signal modeling techniques is presented in this article. To develop large‐signal models of GaN HEMT, a range of commonly used modeling techniques such as artificial neural networks (ANN), random sample consistency (RANSAC), support vector regression (SVR), Gaussian process regression (GPR), decision trees (DTs), and genetic algorithm‐assisted ANN (GA‐ANN) are described and employed. Afterwards, integrated modeling techniques such as Bootstrap aggregation (BA), random forests (RF), AdaBoost, and gradient tree boosting (GTB) are reviewed and tested for their capabilities in developing GaN HEMT LSM. The hyperparameters of each built model are modified using the random search optimization (RSO) method, and five‐fold cross‐validation is used during validation. In order to identify optimal algorithms for nonlinear behavioral modeling of GaN devices, the model prediction results are comprehensively analyzed. [ABSTRACT FROM AUTHOR]

Background: As the population ages, mild cognitive impairment (MCI) and type 2 diabetes mellitus (T2DM) become common conditions that often coexist. Evidence has shown that MCI could lead to reduced treatment compliance, medication management, and self‐care ability in T2DM patients. Therefore, early identification of those with increased risk of MCI is crucial from a preventive perspective. Given the growing utilization of decision trees in prediction of health‐related outcomes, this study aimed to identify MCI in T2DM patients using the decision tree approach. Methods: This hospital‐based case–control study was performed in the Endocrinology Department of Xiangya Hospital affiliated to Central South University between March 2021 and December 2022. MCI was defined based on the Petersen criteria. Demographic characteristics, lifestyle factors, and T2DM‐related information were collected. The study sample was randomly divided into the training and validation sets in a 7:3 ratio. Univariate and multivariate analyses were performed, and a decision tree model was established using the chi‐square automatic interaction detection (CHAID) algorithm to identify key predictor variables associated with MCI. The area under the curve (AUC) value was used to evaluate the performance of the established decision tree model, and the performance of multivariate regression model was also evaluated for comparison. Results: A total of 1001 participants (705 in the training set and 296 in the validation set) were included in this study. The mean age of participants in the training and validation sets was 60.2 ± 10.3 and 60.4 ± 9.5 years, respectively. There were no significant differences in the characteristics between the training and validation sets (p >.05). The CHAID decision tree analysis identified six key predictor variables associated with MCI, including age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy. The established decision tree model had 15 nodes composed of 4 layers, and age is the most significant predictor variable. It performed well (AUC =.75 [95% confidence interval (CI):.71–.78] and.67 [95% CI:.61–.74] in the training and validation sets, respectively), was internally validated, and had comparable predictive value compared to the multivariate logistic regression model (AUC =.76 [95% CI:.72–.80] and.69 [95% CI:.62–.75] in the training and validation sets, respectively). Conclusion: The established decision tree model based on age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy performed well with comparable predictive value compared to the multivariate logistic regression model and was internally validated. Due to its superior classification accuracy and simple presentation as well as interpretation of collected data, the decision tree model is more recommended for the prediction of MCI in T2DM patients in clinical practice. [ABSTRACT FROM AUTHOR]

Soft robots have the advantage of adaptability and flexibility in various scenarios and tasks due to their inherent flexibility and mouldability, which makes them highly promising for real‐world applications. The development of electronic skin (E‐skin) perception systems is crucial for the advancement of soft robots. However, achieving both exteroceptive and proprioceptive capabilities in E‐skins, particularly in terms of decoupling and classifying sensing signals, remains a challenge. This study presents an E‐skin with mixed electronic and ionic conductivity that can simultaneously achieve exteroceptive and proprioceptive, based on the resistance response of conductive hydrogels. It is integrated with soft robots to enable state perception, with the sensed signals further decoded using the machine learning model of decision trees and random forest algorithms. The results demonstrate that the newly developed hydrogel sensing system can accurately predict attitude changes in soft robots when subjected to varying degrees of pressing, hot pressing, bending, twisting, and stretching. These findings that multifunctional hydrogels combine with machine learning to decode signals may serve as a basis for improving the sensing capabilities of intelligent soft robots in future advancements. [ABSTRACT FROM AUTHOR]

Background: Postoperative ileus (POI) continues to be a major cause of morbidity following colorectal surgery. Despite best efforts, the incidence of POI in colorectal surgery remains high (~30%). This study aimed to investigate machine learning techniques to identify risk factors for POI in colorectal surgery patients, to help guide further preventative strategies. Methods: A TRIPOD‐guideline‐compliant retrospective study was conducted for major colorectal surgery patients at a single tertial care centre (2018–2022). The primary outcome was the occurrence of POI, defined as not achieving GI‐2 (outcome measure of time to first stool and tolerance of oral diet) by day four. Multivariate logistic regression, decision trees, radial basis function and multilayer perceptron (MLP) models were trained using a random allocation of patients to training/testing data sets (80/20). The area under the receiver operating characteristic (AUROC) curves were used to evaluate model performance. Results: Of 504 colorectal surgery patients, 183 (36%) experienced POI. Multivariate logistic regression, decision trees, radial basis function and MLP models returned an AUROC of 0.722, 0.706, 0.712 and 0.800, respectively. The MLP model had the highest sensitivity and specificity values. In addition to well‐known risk factors for POI, such as postoperative hypokalaemia, surgical approach, and opioid use, the MLP model identified sarcopenia (ranked 4/30) as a potentially modifiable risk factor for POI. Conclusion: MLP outperformed other models in predicting POI. Machine learning can provide valuable insights into the importance and ranking of specific predictive variables for POI. Further research into the predictive value of preoperative sarcopenia for POI is required. [ABSTRACT FROM AUTHOR]

Proofs of sharp phase transition and noise sensitivity in percolation have been significantly simplified by the use of randomized algorithms, via the OSSS inequality (proved by O'Donnell et al. and the Schramm–Steif inequality for the Fourier‐Walsh coefficients of functions defined on the Boolean hypercube. In this article, we prove intrinsic versions of the OSSS and Schramm–Steif inequalities for functionals of a general Poisson process, and apply these new estimates to deduce sufficient conditions—expressed in terms of randomized stopping sets—yielding sharp phase transitions, quantitative noise sensitivity, exceptional times and bounds on critical windows for monotonic Boolean Poisson functions. Our analysis is based on a new general definition of "stopping set", not requiring any topological property for the underlying measurable space, as well as on the new concept of a "continuous‐time decision tree", for which we establish several fundamental properties. We apply our findings to the k$$ k $$‐percolation of the Poisson Boolean model and to the Poisson‐based confetti percolation with bounded random grains. In these two models, we reduce the proof of sharp phase transitions for percolation, and of noise sensitivity for crossing events, to the construction of suitable randomized stopping sets and the computation of one‐arm probabilities at criticality. This enables us to settle an open problem suggested by Ahlberg et al. (a special case of which was conjectured earlier by Ahlberg et al. on noise sensitivity of crossing events for the planar Poisson Boolean model with random balls whose radius distribution has finite (2+α)$$ \left(2+\alpha \right) $$‐moments and also show the same for planar confetti percolation model with bounded random balls. We also prove that critical probability is 1/2$$ 1/2 $$ for the planar confetti percolation model with bounded, π/2$$ \pi /2 $$‐rotation invariant and reflection invariant random grains. Such a result was conjectured by Benjamini and Schramm in the case of fixed balls and proved by Müller, Hirsch and Ghosh and Roy in the case of balls, boxes and random boxes, respectively; our results contain all previous findings as special cases. [ABSTRACT FROM AUTHOR]

The efficient solution of the AC optimal power flow (OPF) for all cases and electrical grids is not guaranteed. Heuristics, approximations and relaxations have been proposed aplenty, each with pros and cons. This work proposes to solve the AC OPF with binary decision trees (BDTs). The solution starts with the full feasible set of an AC OPF instance, and, iteratively, BDTs tighten the constraints/bounds of that set, to improve the occurring feasible set by the median of the objective function of the preceding set. The medians of the objective function of the progressively tightened feasible sets will converge to the global optimum of the AC OPF. Recent proofs for the estimate performance of top‐down BDT learning heuristics ensure convergence of the method to the global optimum, provided the feasible set is adequately sampled for the BDT training. The recursive implementation of the method and the inductive nature of BDTs may also yield dispatches at multiple optimality levels for the same AC OPF instance, to account for stochastic resources. The performance of the proposed AC OPF solution is assessed over multiple cases by NICTA and the IEEE PES task force on benchmarks for validation of emerging power system algorithms. [ABSTRACT FROM AUTHOR]

Objective: Artificial intelligence (AI)‐based decision support systems (DSS) are utilized in medicine but underlying decision‐making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI‐based DSS in decision‐making tasks as compared to a general population. Methods: We conducted a randomized, blinded study in which members of the Child Neurology Society and American Academy of Neurology were compared to a general population. Participants received recommendations from a DSS via a random assignment of an xAI intervention (decision tree, crowd sourced agreement, case‐based reasoning, probability scores, counterfactual reasoning, feature importance, templated language, and no explanations). Primary outcomes included test performance and perceived explainability, trust, and social competence of the DSS. Secondary outcomes included compliance, understandability, and agreement per question. Results: We had 81 neurology participants with 284 in the general population. Decision trees were perceived as the more explainable by the medical versus general population (P < 0.01) and as more explainable than probability scores within the medical population (P < 0.001). Increasing neurology experience and perceived explainability degraded performance (P = 0.0214). Performance was not predicted by xAI method but by perceived explainability. Interpretation: xAI methods have different impacts on a medical versus general population; thus, xAI is not uniformly beneficial, and there is no one‐size‐fits‐all approach. Further user‐centered xAI research targeting clinicians and to develop personalized DSS for clinicians is needed. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image change detection suffers from poor quality of the difference image and low detection accuracy. Hence, this paper proposes a SAR image change detection method based on a fused difference image and an optimized random forest scheme, termed LRN‐SSARF. Specifically, a fusion operator difference image LRN is proposed, which is generated using a weighted fusion of log‐ratio (LR), ratio (R), and normalized ratio (NoR). This difference image generation method reduces noise's influence. Then, the Otsu algorithm is applied to segment the difference image and select the training samples. The training samples are input into the random forest (RF) model optimised by the sparrow search algorithm (SSA) for training and classification. Finally, the region link is uesd to refine the detection results and generate the final result. The change detection results of six real SAR image scenes highlight that the proposed algorithm has a high detection accuracy, and affords appealing integrity and detailed information about the change regions. Specially, the detection accuracy advantage of the Bangladesh dataset is larger, with the accuracy and Kappa coefficient reaching 98.04% and 92.00%, much higher than the competitor methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The partial label ranking problem is a general interpretation of the preference learning scenario known as the label ranking problem, the goal of which is to learn preference classifiers able to predict a complete ranking with ties over the finite set of labels of the class variable. In this paper, we use unsupervised discretization techniques (equal‐frequency and equal‐width binning) to heuristically select the threshold for the numerical features in the algorithms based on induction of decision trees (partial label ranking trees algorithm). Moreover, we adapt the most well‐known averaging (bootstrap aggregating and random forests) and boosting (adaptive boosting) ensemble methods to the partial label ranking problem, in order to improve the robustness of the built classifiers. We compare the proposed methods with the nearest neighbors‐based algorithm (instance based partial label ranking) over the standard benchmark datasets, showing that our versions of the ensemble methods are superior in terms of accuracy. Furthermore, they are affordable in terms of computational efficiency. [ABSTRACT FROM AUTHOR]

Despite today's developing healthcare technology, conventional colonoscopy is still a gold‐standard method to detect colon abnormalities. Due to the folded structure of the intestine and visual disturbances caused by artifacts, it can be hard for specialists to detect abnormalities during the procedure. Frames that include artifacts such as specular reflection, improper contrast levels from insufficient or excessive illumination gastric juice, bubbles, or residuals should be detected to increase an accurate diagnosis rate. In this work, both conventional machine learning and transfer learning methods have been used to detect non‐informative frames in colonoscopy videos. The conventional machine learning part consists of 5 different types of texture features, which are gray level co‐occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray‐tone difference matrix (NGTDM), focus measure operators (FMOs), and first‐order statistics. In addition to these methods, we utilized 8 different transfer learning models: AlexNet, SqueezeNet, GoogleNet, ShuffleNet, ResNet50, ResNet18, NasNetMobile, and MobileNet. The results showed that FMOs and decision tree combination gave the best accuracy and f‐measure values with almost 89% and 0.79%, respectively, for the conventional machine learning part. When the transfer learning part is taken into account, AlexNet (99.85%) and SqueezeNet (98.80%) have the highest performance metric results. This study shows the potential of both transfer learning and conventional machine learning algorithms to provide fast and accurate non‐informative frame detection to be used during a colonoscopy, which may be considered the initial step in identifying and classifying colon‐related diseases automatically to help guide physicians. [ABSTRACT FROM AUTHOR]

Objectives: To describe a new classification for intraoral scans based on the scan extension and to introduce a decision guideline to choose the scan extension for fabricating tooth‐ and implant‐supported fixed dental prostheses (FDPs). Overview: Multiple operator‐ and patient‐related factors have been identified that can decrease the scanning accuracy of intraoral scanners (IOSs), including scan extension. However, the decision criteria for selecting scan extension for fabricating tooth‐ and implant‐supported restorations is unclear. Based on the extension of the intraoral digital scans, three types of scans can be defined: half‐arch (anterior or posterior), extended half‐arch, and complete‐arch scan. Variables to consider when choosing the scan extension include the number and location of units being restored, as well as the extension and location of edentulous areas. Additionally, the accuracy of the virtual definitive cast and the accuracy of the maxillomandibular relationship captured by using IOSs should be differentiated. Conclusions: A decision tree for selecting the scan extension is presented. The decision is based on the number and location of units being restored, and the extension and location of edentulous areas. Intraoral scans with reduced scan extension are indicated when fabricating tooth‐ and implant‐supported crowns or short‐span fixed prostheses, when the patient does not have more than one missing tooth in the area of the dental arch included in the scan. For the remaining clinical conditions, complete‐arch intraoral scans are recommended. Clinical Significance: Scan extension is a clinician's decision that should be based on the number and location of units being restored and the extension and location of edentulous areas. Intraoral scans with a reduced scan extension is recommended, when possible. [ABSTRACT FROM AUTHOR]

In seismic data acquisition, because of several factors, such as surface barriers, receiver failure, noise contamination and budget control, seismic records often exhibit irregular sampling in the space domain. As corrupted seismic records have a negative effect on seismic migration, inversion and interpretation, seismic trace interpolation is a key step in seismic data pre‐processing. In this paper, we propose a high‐efficiency and high‐precision seismic trace interpolation method for irregularly spatially sampled data by combining an extreme gradient boosting decision tree and principal component analysis in a semi‐supervised learning method. The adjacent trace number, sampling number and amplitudes of the effective seismic data were taken as features to build the training data set for the extreme gradient boosting decision tree. Principal component analysis is applied to remove redundant information and accelerate the training speed. This is different from the traditional trace interpolation method in that the proposed method is data‐driven; therefore, it does not require any assumptions. Compared with other deep learning‐based trace interpolation methods, the proposed method has fewer control parameters and learning labels and a smaller training cost. Experiments using synthetic and field data demonstrated the validity and flexibility of this trace interpolation method. [ABSTRACT FROM AUTHOR]

Breast cancer is possibly the deadliest illness in the world and the risks are gradually increasing. One out of eight women has the chance to be detected with breast cancer in their lifetime. The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human‐understandable terms‐linguistic variables. The Mamdani Fuzzy Rule‐Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels‐ benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state‐of‐the‐art dataset. [ABSTRACT FROM AUTHOR]

Objective: We have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results. Methods: We collected clinicopathological information of RLPS patients from The Surveillance, Epidemiology, and End Results (SEER) database and allocated them into training and validation sets with a 7:3 ratio. Simultaneously, we obtained an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). We performed LASSO regression and multivariate Cox proportional hazards analysis to identify relevant risk factors, which were then combined to develop six machine learning (ML) models: Cox proportional hazards model (Coxph), random survival forest (RSF), ranger, gradient boosting with component‐wise linear models (GBM), decision trees, and boosting trees. The predictive performance of these ML models was evaluated using the concordance index (C‐index), the integrated cumulative/dynamic area under the curve (AUC), and the integrated Brier score, as well as the Cox–Snell residual plot. We also used time‐dependent variable importance, analysis of partial dependence survival plots, and the generation of aggregated survival SHapley Additive exPlanations (SurvSHAP) plots to provide a global explanation of the optimal model. Additionally, SurvSHAP (t) and survival local interpretable model‐agnostic explanations (SurvLIME) plots were used to provide a local explanation of the optimal model. Results: The final ML models are consisted of six factors: patient's age, gender, marital status, surgical history, as well as tumor's histopathological classification, histological grade, and SEER stage. Our prognostic model exhibits significant discriminative ability, particularly with the ranger model performing optimally. In the training set, validation set, and external validation set, the AUC for 1, 3, and 5 year OS are all above 0.83, and the integrated Brier scores are consistently below 0.15. The explainability analysis of the ranger model also indicates that histological grade, histopathological classification, and age are the most influential factors in predicting OS. Conclusions: The ranger ML prognostic model exhibits optimal performance and can be utilized to predict the OS of RLPS patients, offering valuable and crucial references for clinical physicians to make informed decisions in advance. [ABSTRACT FROM AUTHOR]

Conservation decision makers must negotiate social and technical complexities to achieve desired biodiversity outcomes. Quantitative models can inform decision making, by evaluating and predicting management outcomes, so that comparisons can be made between alternative courses of action. However, whether a proposed action is appropriate for implementation, regardless of its contribution to management outcomes, also requires consideration. Existing quantitative models have yet to fully incorporate the suitability of proposed management actions, which hinders their ability to inform decision making.We used gradient boosted decision trees – a machine‐learning technique – to determine the suitability of alternative management actions available to a biodiversity conservation programme. We demonstrate our approach using the Predator Free 2050 programme – a large and complex conservation initiative that seeks to eradicate selected invasive vertebrates from the entirety of New Zealand by 2050. We created a nationally contiguous network of management tools to suppress populations of invasive species across the entire country. We then used our suitability predictions to explore three scenarios for selecting invasive species management tools, based on maximising (a) implementation probability, (b) humaneness and (c) cost‐savings.Our models highlighted that an interplay of factors influence where management tools can potentially be implemented. Our management scenarios revealed what different contiguous management networks could look like for New Zealand over the next 10–15 years as an interim step to achieving Predator Free 2050. Each scenario differed in the tools selected for implementation in different places and in the overall economic costs associated with creating a contiguous management network. Some locations were identified as unsuitable for any existing management tools, indicating that future transformative technologies may be required to create a contiguous network.Synthesis and applications. Conservation decision making must not only consider biodiversity outcomes but also whether selected management actions are appropriate in the first place. Here, we used machine‐learning techniques to determine the suitability of competing managements actions that are proposed to meet biodiversity objectives. Our approach provides an objective, transparent and reproducible framework to determine the suitability of actions at sites across large spatial extents, under complex social and technical constraints. [ABSTRACT FROM AUTHOR]

Aims: To explore the clinico‐sero‐pathological characteristics and risk prediction model of idiopathic inflammatory myopathy (IIM) patients with different muscular perifascicular (PF) changes. Methods: IIM patients in our center were enrolled and the clinico‐sero‐pathological data were retrospectively analyzed. A decision tree model was established through machine learning. Results: There were 231 IIM patients enrolled, including 53 with perifascicular atrophy (PFA), 39 with perifascicular necrosis (PFN), and 26 with isolated perifascicular enhancement of MHC‐I/MHC‐II (PF‐MHCn). Clinically, PFA patients exhibited skin rashes and dermatomyositis‐specific antibodies (DM‐MSAs, 74.5%) except for anti‐Mi2. PFN patients showed the most severe muscle weakness, highest creatine kinase (CK), anti‐Mi2 (56.8%), and anti‐Jo‐1 (24.3%) antibodies. PF‐MHCn patients demonstrated negative MSAs (48.0%) and elevated CK. Histopathologically, MAC predominantly deposited on PF capillaries in PFA but on non‐necrotic myofiber in PFN (43.4% and 36.8%, p < 0.001). MxA expression was least in PF‐MHCn (36.0% vs. 83.0% vs. 63.2%, p < 0.001). The decision tree model could effectively predict different subgroups, especially PFA and PFN. Conclusions: Three types of PF change of IIMs representing distinct clinico‐serological characteristics and pathomechanism. Undiscovered MSAs should be explored especially in PF‐MHCn patients. The three pathological features could be accurately predicted through the decision tree model. [ABSTRACT FROM AUTHOR]

Cable trees are primarily employed in industrial products to facilitate energy transfer and information exchange among various components. When utilizing machines for assembly, it is essential to convert the wiring plan into a sequence of cable insertion operations executed by the machine under various constraints. This poses a combinatorial optimization problem. In this domain, constraint programming (CP) solvers often exhibit outstanding performance by leveraging their robust problem‐modelling capabilities, excellent scalability, and precise solving capabilities. However, CP solvers may achieve various performances for different problem instances. Selecting the most suitable CP solver for each problem instance is crucial. This paper introduces an automatic selection algorithm for CP solvers to solve the cable tree wiring problem (CTW). Firstly, a scoring system is used to conduct an in‐depth analysis and compare four well‐known CP solvers: CPLEX, Chuffed, OR‐Tools, and Gurobi. The results indicate that OR‐Tools and CPLEX outperform other solvers in performance. Moreover, these two solvers exhibit complementary advantages in quickly finding optimal and feasible solutions within specified time limits. Therefore, CP and machine learning are ingeniously integrated, harnessing their complementary advantages. 4240 instances covering various scenarios are randomly generated to form the problem space. This method incorporates decision trees, random forests, K‐nearest neighbours, and naive Bayes, utilizing these four machine learning techniques. The proposed method can achieve better results than traditional single CP solvers. Among all the evaluated machining learning techniques, the automatic solver selection methods based on decision trees and random forests can achieve accuracy rates of 91.29% and 84.15%, respectively. [ABSTRACT FROM AUTHOR]

Online consumer lending has recently been growing rapidly, but it faces high credit risk. For this problem, developing powerful credit scoring models has become an effective solution and can be achieved from three aspects: modeling approach, data source, and evaluation measure. This paper proposes a novel model that departs from those in previous studies in threefold. First, a heterogeneous deep forest model that combines deep learning architecture and tree‐based ensemble classifiers is proposed as the modeling approach. Second, a Bayesian‐based macroeconomic variable optimization method is developed to determine the macroeconomic variables and the corresponding lag term, and the selected macroeconomic variables are used as supplementary data source for modeling. Lastly, a series of capital charge error measures is proposed to evaluate credit scoring models from a regulatory perspective. The proposal is evaluated on multiple large datasets under performance measures on predictive accuracy, profitability, and capital charge errors. Frequentist and Bayesian nonparametric significance tests are used to examine the statistical significance of heterogeneous deep forest and benchmarks. Three main conclusions can be reached from the comparison. First, heterogeneous deep forest significantly outperforms the industry benchmarks over all the evaluation measures. Second, the predictive performance is enhanced after incorporating the selected macroeconomic variables and the corresponding lag, and the result remains robust under cross‐validation and forward‐chaining validation. Third, the capital charge errors reflect the model performance from a regulatory perspective and thus lead to different rankings from those when evaluating predictive accuracy and profitability. [ABSTRACT FROM AUTHOR]

Classical supervised methods like linear regression and decision trees are not completely adapted for identifying impacting factors on a response variable corresponding to zero‐inflated proportion data (ZIPD) that are dependent, continuous and bounded. In this article we propose a within‐block permutation‐based methodology to identify factors (discrete or continuous) that are significantly correlated with ZIPD, we propose a performance indicator quantifying the percentage of correlation explained by the subset of significant factors, and we show how to predict the ranks of the response variables conditionally on the observation of these factors. The methodology is illustrated on simulated data and on two real data sets dealing with epidemiology. In the first data set, ZIPD correspond to probabilities of transmission of Influenza between horses. In the second data set, ZIPD correspond to probabilities that geographic entities (eg, states and countries) have the same COVID‐19 mortality dynamics. [ABSTRACT FROM AUTHOR]

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 (r2 = 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. [ABSTRACT FROM AUTHOR]

Background: Current methods for developing clinical practice guidelines have several limitations: they are characterised by the "black box" operation—a process with defined inputs and outputs but an incomplete understanding of its internal workings; they have "the integration problem"—a lack of framework for explicitly integrating factors such as patient preferences and trade‐offs between benefits and harms; they generate one recommendation at a time that typically are not connected in a coherent analytical framework; and they apply to "average" patients, while clinicians and their patients seek advice tailored to individual circumstances. Methods: We propose augmenting the current guideline development method by converting evidence‐based pathways into fast‐and‐frugal decision trees (FFTs) and integrating them with generalised decision curve analysis to formulate clear, individualised management recommendations. Results: We illustrate the process by developing recommendations for the management of heparin‐induced thrombocytopenia (HIT). We converted evidence‐based pathways for HIT, developed by the American Society of Hematology, into an FFT. Here, we consider only thrombotic complications and major bleeding. We leveraged the predictive potential of FFTs to compare the effects of argatroban, bivalirudin, fondaparinux, and direct oral anticoagulants (DOACs) using generalised decision curve analysis. We found that DOACs were superior to other treatments if the FFT‐predicted probability of HIT exceeded 3%. Conclusions: The proposed analytical framework connects guidelines, pathways, FFTs, and decision analysis, offering risk‐tailored personalised recommendations and addressing current guideline development critiques. [ABSTRACT FROM AUTHOR]

Objective: To develop a comprehensive decision‐making tree for evaluating mid‐facial peri‐implant soft tissue dehiscence in the esthetic zone and provide a systematic approach for assessing various clinical case scenarios, determining appropriate treatment strategies, and considering factors such as the need for soft tissue augmentation, prosthetic changes, or implant removal. Clinical Considerations: This clinical decision tree illustrates numerous case scenarios with various esthetic complications around an esthetically compromised, but clinically healthy single implant and provides clinicians with possible solutions as a predictable map for horizontal and vertical soft tissue augmentation in order to manage different clinical circumstances. According to current evidence, the key to treating such esthetic complications is the use of an adequate pre‐surgical prosthetic interdisciplinary approach with proper surgical techniques in order to optimize soft tissue dimensions and create better esthetic results. This may be accomplished through a purely surgical, combination of surgical and prosthetic, or purely prosthetic approaches. Conclusions: The present report describes a series of successfully treated peri‐implant esthetic complication cases in accordance with the decision‐making tree that the authors recommend in order to achieve better long‐term esthetic outcomes. Clinical Significance: The combination of adequate pre‐surgical prosthetic interdisciplinary collaboration and proper surgical technique is critical in the optimization of sufficient soft tissue dimensions and contributes to a more highly esthetic result. This study demonstrates a clinical decision‐making tree to provide comprehensive, effective therapy of an esthetically compromised dental implant by using one of the following approaches: purely prosthetic, purely surgical, or a combination of surgical and prosthetic with or without abutment removal. [ABSTRACT FROM AUTHOR]

The commutation failure is the most prevalent fault in line‐commutated converter based high‐voltage direct current systems, which may result in transient overvoltage on the sending‐side system. Overvoltage level evaluation has become a crucial task for power industries to assess the tripping risk of large‐scale wind turbines and implement effective stability control measures. In this paper, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented firstly, along with an analytical expression for the peak value of transient overvoltage. Secondly, decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. On this basis, an overvoltage analysis method integrating the model‐driven and data‐driven techniques is proposed, and the improved DT algorithm is applied to fast error correction, enhancing the interpretability of regression prediction results. Case studies were performed in the simulation system of Northwest China local power grid with transient overvoltage problems, and the simulation results verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The aim of this pre‐ and post‐interventional study was to analyze the cost‐effectiveness of the multicomponent fall prevention program in hospitalized patients. To achieve this aim, cost‐effectiveness analysis performed using decision tree modeling was compared with the implementation of the fall prevention program and usual care. The primary outcome was the number of patient falls. The uncertainty in cost and effectiveness data was evaluated using one‐way sensitivity analysis, best–worst‐case scenario analysis, and probabilistic sensitivity analysis. According to cost‐effectiveness analysis, implementation of the fall prevention program was dominantly cost‐effective. As a result of the probabilistic sensitivity analysis, it was revealed that, even if willing‐to‐pay per‐fall prevented value was 0, the probability of being cost‐effective was 54.4% for the fall prevention program. Economic evaluation results showed that implementing the multicomponent fall prevention program was dominantly cost‐effective in hospitalized patients. Nurses and nurse managers can benefit from economic evaluations in their decision‐making processes to implement fall prevention programs. [ABSTRACT FROM AUTHOR]

Text classification (TC) is a very crucial task in this century of high‐volume text datasets. Feature selection (FS) is one of the most important stages in TC studies. In the literature, numerous feature selection methods are recommended for TC. In the TC domain, filter‐based FS methods are commonly utilized to select a more informative feature subsets. Each method uses a scoring system that is based on its algorithm to order the features. The classification process is then carried out by choosing the top‐N features. However, each method's feature order is distinct from the others. Each method selects by giving the qualities that are critical to its algorithm a high score, but it does not select by giving the features that are unimportant a low value. In this paper, we proposed a novel filter‐based FS method namely, brilliant probabilistic feature selector (BPFS), to assign a fair score and select informative features. While the BPFS method selects unique features, it also aims to select sparse features by assigning higher scores than common features. Extensive experimental studies using three effective classifiers decision tree (DT), support vector machines (SVM), and multinomial naive bayes (MNB) on four widely used datasets named Reuters‐21,578, 20Newsgroup, Enron1, and Polarity with different characteristics demonstrate the success of the BPFS method. For feature dimensions, 20, 50, 100, 200, 500, and 1000 dimensions were used. The experimental results on different benchmark datasets show that the BPFS method is more successful than the well‐known and recent FS methods according to Micro‐F1 and Macro‐F1 scores. [ABSTRACT FROM AUTHOR]

Objective: To determine the cost‐effectiveness of a virtual version of the Body Project (vBP), a cognitive dissonance‐based program, to prevent eating disorders (ED) among young women with a subjective sense of body dissatisfaction in the Swedish context. Method: A decision tree combined with a Markov model was developed to estimate the cost‐effectiveness of the vBP in a clinical trial population of 149 young women (mean age 17 years) with body image concerns. Treatment effect was modeled using data from a trial investigating the effects of vBP compared to expressive writing (EW) and a do‐nothing alternative. Population characteristics and intervention costs were sourced from the trial. Other parameters, including utilities, treatment costs for ED, and mortality were sourced from the literature. The model predicted the costs and quality‐adjusted life years (QALYs) related to the prevention of incidence of ED in the modeled population until they reached 25 years of age. The study used both a cost‐utility and return on investment (ROI) framework. Results: In total, vBP yielded lower costs and larger QALYs than the alternatives. The ROI analysis denoted a return of US $152 for every USD invested in vBP over 8 years against the do‐nothing alternative and US $105 against EW. Discussion: vBP is likely to be cost‐effective compared to both EW and a do‐nothing alternative. The ROI from vBP is substantial and could be attractive information for decision makers for implementation of this intervention for young females at risk of developing ED. Public Significance: This study estimates that the vBP is cost‐effective for the prevention of eating disorders among young women in the Swedish setting, and thus is a good investment of public resources. [ABSTRACT FROM AUTHOR]

Soybean is one of the most important oilseed crops grown worldwide. However, abiotic stresses such as drought and salinity can seriously affect soybean production, especially in tropical climate conditions. To evaluate the adaptability and stability of soybean genotypes under abiotic stress conditions, some studies have proposed a multitrait tool to select stress‐tolerant soybean genotypes through a multitrait stability index (MTSI). This index can be used under stressful environmental conditions to quantify the genotypic stability of soybean cultivars. Our study is based on an unprecedented approach, where we propose to use a machine learning algorithm called 'Random Forest' to obtain a classification model based on a decision tree algorithm. The decision tree data structure can be used even by nonexperts facilitating the decision‐making process for genotype selection. The proposed model evaluated the importance of six shoot and root morphological variables and predicted from which controlled growth environment the soybean plants originated. Using this model more than 73% of the genotypic patterns were learned correctly. Besides that, this model can also predict and rank the most critical variables in the development of soybean genotypes, having obtained results very similar to recent field research. The research is important for plant breeders who seek an early selection of soybean seedlings for drought and saline stresses. [ABSTRACT FROM AUTHOR]

Even though it is well known that for most relevant computational problems, different algorithms may perform better on different classes of problem instances, most researchers still focus on determining a single best algorithmic configuration based on aggregate results such as the average. In this paper, we propose integer programming‐based approaches to build decision trees for the algorithm selection problem. These techniques allow the automation of three crucial decisions: (a) discerning the most important problem features to determine problem classes, (b) grouping the problems into classes, and (c) selecting the best algorithm configuration for each class. To evaluate this new approach, extensive computational experiments were executed using the linear programming algorithms implemented in the COIN‐OR branch‐and‐cut solver across a comprehensive set of instances, including all MIPLIB benchmark instances. The results exceeded our expectations. While selecting the single best parameter setting across all instances decreased the total running time by 22%, our approach decreased the total running time by 40% on average across 10‐fold cross‐validation experiments. These results indicate that our method generalizes quite well and does not overfit. [ABSTRACT FROM AUTHOR]

Purpose: Develop an efficient, interactive, and instructive checklist document for the management of implanted electronic medical devices in a multimodality radiotherapy clinic. Methods: The built-in scripting and interactivity of a popular commercial word processor was used to develop an interactive document that changes the information presented to the user based on drop-down selections. The interactivity and scripting were compatible with the radiation oncology information system (ROIS) which allows the document to be accessible by all team members and serve as a permanent record in a patient's electronic chart. Results: The final interactive document,which was clinically deployed after beta testing with a group consisting of nurses and medical physicists, presents information and action plans to the user based on multiple departmental medical device decision trees that are specific to the combination of device, treatment modality, rhythm-pacing dependence for cardiac devices, and distance from the device to the treatment volume. Conclusion: A script-enabled interactive document was developed for a busy multimodality clinic, condensing multiple comprehensive departmental guidelines spanning multiple device types and treatment modalities into a single interactive checklist accessible within the ROIS. Given the wide accessibility of the commercial word processor, this approach could be adopted by other clinics to streamline their own respective workflows. [ABSTRACT FROM AUTHOR]

The current study aims to determine the programming performance (low and high) of the students between the ages of 12–24 who receive programming education, by descriptive analysis, to determine the current situation according to various variables and to redict them with machine learning algorithms. Thus, the change in programming performance, computational identity, computational thinking perspective, programming empowerment, and programming anxiety according to various variables was examined. The performances of different algorithms were compared in estimating the low and high programming performance of these variables. The research involved 763 students who were between the ages of 12 and 24 and had received programming education. Different scales were used to collect the opinions of students who received programming education. Descriptive analyses, one‐way analysis of variance (ANOVA), and machine learning algorithms were used in the analysis of the data set. Analyzes were made in Statistical Package for the Social Sciences (SPSS) and Knime software. Decision trees, k‐nearest neighbors, support vector machines, random forest, Naive Bayes classifiers, and logistic regression were used in the study. Students' programming performance, computational identity, computational thinking perspective, and programming empowerment mean scores differ in terms of gender and educational level variables. Research variables do not differ statistically in terms of academic success level variables. According to the programming performance level, students' computational identity, computational thinking perspective, and programming empowerment scores differ. No significant difference was found between programming anxiety scores. Decision trees of the algorithm with the highest accuracy result according to low and high programming performance conditions are [(0.966), (0.966)], respectively. The fact that these obtained scores are above 90% can be interpreted as sufficient estimation performance. [ABSTRACT FROM AUTHOR]

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%; χ2 = 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 (χ2 = 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. [ABSTRACT FROM AUTHOR]

Administration of a new drug candidate in a first‐in‐human (FIH) clinical trial is a particularly challenging phase in drug development and is especially true for immunomodulators, which are a diverse and complex class of drugs with a broad range of mechanisms of action and associated safety risks. Risk is generally greater for immunostimulators, in which safety concerns are associated with acute toxicity, compared to immunosuppressors, where the risks are related to chronic effects. Current methodologies for FIH dose selection for immunostimulators are focused primarily on identifying the minimum anticipated biological effect level (MABEL), which has often resulted in sub‐therapeutic doses, leading to long and costly escalation phases. The Health and Environmental Sciences Institute (HESI) – Immuno‐Safety Technical Committee (ITC) organized a project to address this issue through two complementary approaches: (i) an industry survey on FIH dose selection strategies and (ii) detailed case studies for immunomodulators in oncology and non‐oncology indications. Key messages from the industry survey responses highlighted a preference toward more dynamic PK/PD approaches as in vitro assays are seemingly not representative of true physiological conditions for immunomodulators. These principles are highlighted in case studies. To address the above themes, we have proposed a revised decision tree, which expands on the guidance by the IQ MABEL Working Group (Leach et al. 2021). This approach facilitates a more refined recommendation of FIH dose selection for immunomodulators, allowing for a nuanced consideration of their mechanisms of action (MOAs) and the associated risk‐to‐benefit ratio, among other factors. [ABSTRACT FROM AUTHOR]

The learning effect of students is crucial for assessing teaching quality, thus playing a significant role in teaching management. Predicting student achievement is a major challenge in understanding the learning effect of students. Currently, many studies have utilized machine learning methods such as the decision tree algorithms C4.5, ID3, CART, J48, random forest, and others. However, few studies have explored the use of the Bagging algorithm in this field. Therefore, this study proposes a classification prediction method for student achievement based on the Bagging‐CART algorithm. Initially, the student achievement data is preprocessed, and the Apriori method is applied to mine the strongly associated dataset. The optimal hyper‐parameters are determined through grid search to train and predict the Bagging‐CART algorithm. Furthermore, the CART, J48, and Bagging‐CART algorithms are trained, and their evaluation indicators are compared using a confusion matrix. The results indicate that the Bagging‐CART model achieves an accuracy of 98.16%, a recall rate of 91.80%, a precision of 90.83%, and an F1 score of 94.87%. In comparison, the accuracy, precision, and F1 scores are higher than those obtained with CART and J48. Although the recall rate is slightly lower than that of CART by 0.26%, it is 0.52% higher than that of J48. Consequently, this method demonstrates strong predictive capabilities and introduces a new reference method for evaluating students' learning effect. [ABSTRACT FROM AUTHOR]

Background: There is little evidence regarding risk prediction for surgical site infection (SSI) after lower third molar (L3M) surgery. Methods: We conducted a nested case–control study to develop a multivariable logistic model for predicting the risk of SSI after L3M surgery. Data were obtained from Hokkaido University Hospital from April 2013 to March 2020. Multiple imputation was applied for the missing values. We conducted decision tree (DT) analysis to evaluate the combinations of factors affecting SSI risk. Results: We identified 648 patients. The final model retained the available distal space (Pell & Gregory II [p = 0.05], Pell & Gregory III [p < 0.01]), depth (Pell & Gregory B [p < 0.01], Pell & Gregory C [p < 0.01]), surgeon's experience (3–10 years [p = 0.25], <3 years [p < 0.01]), and simultaneous extraction of both L3M [p < 0.01]; the concordance‐statistic was 0.72. The DT analysis demonstrated that patients with Pell and Gregory B or C and simultaneous extraction of both L3M had the highest risk of SSI. Conclusions: We developed a model for predicting SSI after L3M surgery with adequate predictive metrics in a single center. This model will make the SSI risk prediction more accessible. [ABSTRACT FROM AUTHOR]

Coordinated policies and effective global environmental governance are needed to address the global biodiversity crisis. Human dimensions like geopolitics influence conservation decision‐making and outcomes. The importance of considering these complex social factors is heightened in an era of renewed great‐power politics, as the intensifying US–China rivalry has direct implications for environmental governance and biodiversity outcomes. Can the US–China rivalry and its confrontational dynamics be channeled into conservation policymaking to improve biodiversity outcomes? Drawing from international relations and policy studies, policy diffusion theory can provide conservationists with useful insights into the interdependency of policy decisions. Here, we examine the four mechanisms—competition, coercion, learning, and emulation—of the classic model of policy diffusion theory in the context of environmental policymaking. We explore a case study for each mechanism to illustrate how it can benefit biodiversity conservation, and point to examples of relevant policies and actions that could improve outcomes. To operationalize this concept for conservation, we present a decision tree that conservationists can use to determine the most relevant policy diffusion mechanism in different policy contexts. Upon determining the appropriate mechanism, conservationists can take further steps to intentionally trigger the mechanism and catalyze conservation policy diffusion across jurisdictions. [ABSTRACT FROM AUTHOR]

Injury surveillance data indicate that collegiate athletes are at greater risk for lower extremity (LE) injuries following sports‐related concussion (SRC). While the association between SRC and LE injury appears to be clinically relevant up to 1‐year post‐SRC, little evidence has been provided to determine possible mechanistic rationales. Thus, we aimed to compare collegiate athletes with a history of SRC to matched controls on biomechanical and cognitive performance measures associated with LE injury risk. Athletes with a history of SRC (n = 20) and matched controls (n = 20) performed unanticipated bilateral land‐and‐cut tasks and cognitive assessments. Group‐based analyses (ANOVA) and predictive modeling (C5.0 decision tree algorithm) were used to compare group differences on biomechanical and cognitive measures. Collegiate athletes with a history of SRC demonstrated approximately six degrees less peak knee flexion on both dominant (p = 0.03, d = 0.71) and nondominant (p = 0.02, d = 0.78) limbs during the land‐and‐cut tasks compared to controls. Verbal Memory, knee flexion, and Go/No Go total score (C5.0 decision tree algorithm) were identified as the strongest indicators of previous SRC injury history. Reduced knee flexion during sport‐specific land‐and‐cut tasks may be a mechanism for increased LE injury risk in athletes with a history of SRC. There appears to be multiple biomechanical and cognitive predictors for identifying previous SRC in collegiate athletes, providing evidence to support a multifactorial SRC management strategy to reduce future injury risk. [ABSTRACT FROM AUTHOR]