Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

Clinical prediction rules could help identify children at risk of slowed growth after an episode of diarrheal illness, and these rules may be generalizable to all children, regardless of diarrhea status.

Background/Objective: Subarachnoid hemorrhage (SAH) is associated with high morbidity and mortality rates, necessitating prognostic algorithms to guide decisions. Our study evaluates the use of machine learning (ML) models for predicting 1-month and 1-year mortality among SAH patients using national electronic health records (EHR) system. Methods: Retrospective cohort of 29,274 SAH patients, identified through national EHR system from January 2017 to December 2022, was analyzed, with mortality data obtained from central civil registration system in Türkiye. Variables included (n = 102) pre- (n = 65) and post-admission (n = 37) data, such as patient demographics, clinical presentation, comorbidities, laboratory results, and complications. We employed logistic regression (LR), decision trees (DTs), random forests (RFs), and artificial neural networks (ANN). Model performance was evaluated using area under the curve (AUC), average precision, and accuracy. Feature significance analysis was conducted using LR. Results: The average age was 56.23 ± 16.45 years (47.8% female). The overall mortality rate was 22.8% at 1 month and 33.3% at 1 year. One-month mortality increased from 20.9% to 24.57% (p < 0.001), and 1-year mortality rose from 30.85% to 35.55% (p < 0.001) in the post-COVID period compared to the pre-COVID period. For 1-month mortality prediction, the ANN, LR, RF, and DT models achieved AUCs of 0.946, 0.942, 0.931, and 0.916, with accuracies of 0.905, 0.901, 0.893, and 0.885, respectively. For 1-year mortality, the AUCs were 0.941, 0.927, 0.926, and 0.907, with accuracies of 0.884, 0.875, 0.861, and 0.851, respectively. Key predictors of mortality included age, cardiopulmonary arrest, abnormal laboratory results (such as abnormal glucose and lactate levels) at presentation, and pre-existing comorbidities. Incorporating post-admission features (n = 37) alongside pre-admission features (n = 65) improved model performance for both 1-month and 1-year mortality predictions, with average AUC improvements of 0.093 ± 0.011 and 0.089 ± 0.012, respectively. Conclusions: Our study demonstrates the effectiveness of ML models in predicting mortality in SAH patients using big data. LR models’ robustness, interpretability, and feature significance analysis validate its importance. Including post-admission data significantly improved all models’ performances. Our results demonstrate the utility of big data analytics in population-level health outcomes studies.

BackgroundHeart failure (HF) impacts nearly 6 million individuals in the U.S., with a projected 46% increase by 2030, is creating significant healthcare burdens. Predictive models, particularly machine learning (ML)-based models, offer promising solutions to identify patients at greater risk of adverse outcomes, such as mortality and hospital readmission. This review aims to assess the effectiveness of ML models in predicting HF-related outcomes, with a focus on their potential to improve patient care and clinical decision-making. We aim to assess how effectively machine learning models predict mortality and readmission in heart failure patients to improve clinical outcomes.MethodThe study followed PRISMA 2020 guidelines and was registered in the PROSPERO database (CRD42023481167). We conducted a systematic search in PubMed, Scopus, and Web of Science databases using specific keywords related to heart failure, machine learning, mortality and readmission. Extracted data focused on study characteristics, machine learning details, and outcomes, with AUC or c-index used as the primary outcomes for pooling analysis. The PROBAST tool was used to assess bias risk, evaluating models based on participants, predictors, outcomes, and statistical analysis. The meta-analysis pooled AUCs for different machine learning models predicting mortality and readmission. Prediction accuracy data was categorized by timeframes, with high heterogeneity determined by an I² value above 50%, leading to a random-effects model when applicable. Publication bias was assessed using Egger’s and Begg’s tests, with a p-value below 0.05 considered significantResultA total of 4,505 studies were identified, and after screening, 64 were included in the final analysis, covering 943,941 patients. Of these, 40 studies focused on mortality, 17 on readmission, and 7 on both outcomes. In total, 346 machine learning models were evaluated, with the most common algorithms being random forest, logistic regression, and gradient boosting. The neural network model achieved the highest overall AUC for mortality prediction (0.808), while the support vector machine performed best for readmission prediction (AUC 0.733). The analysis revealed a significant risk of bias, primarily due to reliance on retrospective data and inadequate sample size justification.ConclusionIn conclusion, this review emphasizes the strong potential of ML models in predicting HF readmission and mortality. ML algorithms show promise in improving prognostic accuracy and enabling personalized patient care. However, challenges like model interpretability, generalizability, and clinical integration persist. Overcoming these requires refined ML techniques and a robust regulatory framework to enhance HF outcomes.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

The conditional logistic regression model (Biometrics 1982; 38:661-672) provides a convenient method for the assessment of qualitative or quantitative covariate effects on risk in a study with matched sets, each containing a possibly different number of cases and controls. The conditional logistic likelihood is identical to the stratified Cox proportional hazards model likelihood, with an adjustment for ties (J. R. Stat. Soc. B 1972; 34:187-220). This likelihood also applies to a nested case-control study with multiply matched cases and controls, selected from those at risk at selected event times. Herein the distribution of the score test for the effect of a covariate in the model is used to derive simple equations to describe the power of the test to detect a coefficient theta (log odds ratio or log hazard ratio) or the number of cases (or matched sets) and controls required to provide a desired level of power. Additional expressions are derived for a quantitative covariate as a function of the difference in the assumed mean covariate values among cases and controls and for a qualitative covariate in terms of the difference in the probabilities of exposure for cases and controls. Examples are presented for a nested case-control study and a multiply matched case-control study.

Familial Hypercholesterolemia (FH) is an inherited disorder of lipid metabolism, characterized by increased low density lipoprotein cholesterol (LDLc) levels. The main purpose of the current work was to explore alternative classification methods to traditional clinical criteria for FH diagnosis, based on several biochemical and biological indicators. Logistic regression (LR), decision tree (DT), random forest (RF) and naive Bayes (NB) algorithms were developed for this purpose, and thresholds were optimized by maximization of Youden index (YI). All models presented similar accuracy (Acc), specificity (Spec) and positive predictive values (PPV). Sensitivity (Sens) and G-mean values were significantly higher in LR and RF models, compared to the DT. When compared to Simon Broome (SB) biochemical criteria for FH diagnosis, all models presented significantly higher Acc, Spec and G-mean values (p < 0.01), and lower negative predictive value (NPV, p < 0.05). Moreover, LR and RF models presented comparable Sens values. Adjustment of the cut-off point by maximizing YI significantly increased Sens values, with no significant loss in Acc. The obtained results suggest such classification algorithms can be a viable alternative to be used as a widespread screening method. An online application has been developed to assess the performance of the LR model in a wider population.

A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees

Assessing treatment switch among patients with multiple sclerosis: A machine learning approach

<title>Abstract</title> Objective To construct various radiomics models based on CT venous phase images for the quantitative assessment of gastric cancer (GC) invasion depth into the gastric wall, and to compare the diagnostic performance of different models in distinguishing early (T1-T2 stage) from advanced (T3-T4 stage) GC, aiming to identify the optimal model and evaluate its potential for clinical application. Methods In this retrospective study, 223 pathologically confirmed GC patients (66 early-stage, 157 advanced-stage) treated between January 2022 and May 2025 were enrolled. Patients were allocated into a training set (n = 156) and an independent test set (n = 67) through stratified random sampling at a 7:3 ratio. All patients underwent enhanced CT within one week prior to surgery. Venous phase images were selected, and three-dimensional regions of interest (ROIs) encompassing the tumor were manually delineated using 3D Slicer for radiomics feature extraction. In the training set, feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Four machine learning models were subsequently constructed: Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), and Support Vector Machine (SVM). Model optimization was conducted via 5-fold cross-validation. Performance was evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, confusion matrices, and decision curve analysis (DCA). Results A total of 148 radiomics features were extracted, from which 11 key features were selected by LASSO. In the training set under 5-fold cross-validation, the LR, RF, SVM, and XGB models demonstrated strong discriminatory ability, with AUCs of 0.912, 0.910, 0.899, and 0.880, respectively. In the independent test set, the LR model exhibited the best overall performance: AUC 0.912 (95% CI: 0.853–0.948), accuracy 0.791, sensitivity 0.787, specificity 0.800, F1-score 0.841, and average precision (AP) 0.966. DeLong's test revealed statistically significant differences in AUC among models (all P &lt; 0.001), though the difference between LR and RF was minimal (0.0021, 95% CI: − 0.0459–0.0502). All models showed good calibration (Hosmer–Lemeshow test P &gt; 0.05). DCA indicated that the LR model provided higher net clinical benefit within a threshold probability range of 0.3–0.8. Conclusion Among the multiple radiomics models constructed based on CT venous phase images, the LR model demonstrated the best performance in distinguishing early from advanced GC T stages, showing favorable diagnostic efficacy and clinical utility. It can serve as a quantitative auxiliary tool for preoperative staging and treatment decision-making, providing an objective basis for precise GC diagnosis and treatment.

Predictive models in analytical CRM (customer relationship management) are closely related to the customer’s life cycle. Prediction of binary dependent variable refers to the most common areas such as customer acquisition, customer development (cross-selling and up-selling), and customer retention (churn analysis). While building static predictive models one usually applies decision trees, logistic regression, support vector machines or ensemble methods such as different algorithms of boosted decision trees or random forest. Recently one can observe increasing use of hybrid models in the analytical CRM, i.e. those that combine several different analytical tools, e.g. cluster analysis with decision trees, genetic algorithms with neural networks, or decision trees with logistic regression. The purpose of this paper is to compare the results obtained by using hybrid predictive CART-logit models with single decision tree models and logistic regression models. All analyses have been conducted on the basis of data sets relating to analytical CRM.

Summary for Social Media@AliciaCastongu2, @FazalZaidi9, @oozaidat, @Mouhammad_JumaaObjectiveMachine learning (ML) algorithms have emerged as powerful predictive tools in the field stroke. Here, we examine the predictive accuracy of ML models for predicting functional outcomes using 24‐hour post‐treatment characteristics in the Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke (STRATIS) Registry.MethodsML models, adaptive boost, random forest (RF), classification and regression trees (CART), C5.0 decision tree (C5.0), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR), and traditional LR models were used to predict 90‐day functional outcome (modified Rankin Scale score 0–2). Twenty‐four‐hour National Institutes of Health Stroke Scale (NIHSS) was examined as a continuous or dichotomous variable in all models. Model accuracy was assessed using the area under characteristic curve (AUC).ResultsThe 24‐hour NIHSS score was a top‐predictor of functional outcome in all models. ML models using the continuous 24‐hour NIHSS scored showed moderate‐to‐good predictive performance (range mean AUC: 0.76–0.92); however, RF (AUC: 0.92 ± 0.028) outperformed all ML models, except LASSO (AUC: 0.89 ± 0.023, p = 0.0958). Importantly, RF demonstrated a significantly higher predictive value than LR (AUC: 0.87 ± 0.031, p = 0.048) and traditional LR (AUC: 85 ± 0.06, p = 0.035) when using the 24‐hour continuous NIHSS score. Predictive accuracy was similar between the 24‐hour NIHSS score dichotomous and continuous ML models.InterpretationIn this substudy, we found similar predictive accuracy for functional outcome when using the 24‐hour NIHSS score as a continuous or dichotomous variable in ML models. ML models had moderate‐to‐good predictive accuracy, with RF outperforming LR models. External validation of these ML models is warranted. ANN NEUROL 2023;93:40–49

Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression, Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calculated using clincalc analysis, with alpha and beta values 0.05 and 0.5, 95% confidence, pretest power 80% and enrolment ratio 1. Results: The Novel Random Forest Algorithm (92.13%), Support Vector Machine (62.51%), Logistic Regression (84.89%), Decision Tree (86.25%) classifiers produce respectively. SVM, RF exists a statistically significant difference between the two groups (p=0.001,p=.004;p&lt;0.05).LR, RF exists a statistically insignificant difference between the two groups (p=.103, P=.080;p&gt;0.05) both with confidence interval 95%. Hence Random forest is better than SVM, RF, DT classifiers. Conclusion: The results show that the performance of RF is better when compared with SVM, LR and DT in terms of both precision and accuracy.

Hospital Readmission is considered as an effective measurement of service and care provided within the hospital. Emergency readmission to hospital is frequently used as a measure of the quality of a hospital because a high proportion of readmissions should be preventable if the preceding care is adequate. The objective of this study to develop a model to predict 30-day hospital readmission. We have data of 1-lac diabetes patients with 50 features. We used machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, Adaboost and XGBoost for prediction. We achieved the highest accuracy 94% using Random forest among all other algorithms. The results from this study are encouraging and can help healthcare providers to improve their services.

Peer review is a critical process in the publication of scientific findings; trainees and young investigators, however, have few opportunities to learn systematically how to review manuscripts. Journal editors have an opportunity to engage trainees and young investigators in the peer review process early during their career. Methods of Information in Medicine, an official journal of the International Medical Informatics Association, is initiating a Student Editorial Board. The journal invites applications from international graduate and post-doctoral training programs that have a focus on health informatics, biomedical informatics, or a related field. Each year up to six trainees will be invited to join the Student Editorial Board. The trainees will go through a mentored training experience that includes an active involvement in the various aspects of peer review during their one to two-year term of appointment. The journal expects that the Student Editorial Board will benefit trainees and young investigators in becoming skilled reviewers and engaged peers who can offer professional, constructive, and informative feedback and enhance the process of scientific communication.

Machine learning-driven prediction of readmission risk in heart failure patients with diabetes: synergistic assessment of inflammatory and metabolic biomarkers.