arXiv Machine Learning
Jun 10
Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports
arXiv:2606. 10725v1 Announce Type: new Abstract: Background.
Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES
The study presents a clinically interpretable machine‑learning framework that estimates biological age (BA) using a CatBoost regression model trained on 44 diverse predictors from NHANES data. SHAP values are leveraged not only for visual interpretation but also through an automated quantitative procedure that identifies feature‑value ranges linked to the lowest and highest BA contributions, including zero‑crossing thresholds. The resulting BA estimates correlate strongly with chronological age and highlight key predictors such as blood urea nitrogen, serum glucose, and vigorous activity, offering transparent, reproducible insights for precision health research.
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arXiv AI
Jun 10
Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages
arXiv:2606. 09860v1 Announce Type: cross Abstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks.
arXiv AI
Sep 1
Predicting Future Organ Dysfunction in ICU Patients Using Temporal Convolutional Networks on MIMIC-IV Data
arXiv:2608.29301v1 Announce Type: new Abstract: Predicting future organ dysfunction in Intensive Care Unit (ICU) patients is critical for early clinical intervention, yet existing machine learning ap...
arXiv Machine Learning
Sep 18
Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarization
The paper introduces a statistically grounded framework for interpretable, rule-based clinical classification using Bernoulli Naïve Bayes (BNB). It employs supervised chi‑square‑guided binarization to convert continuous medical variables into binary indicators, enabling BNB to handle continuous data while maintaining transparency. On three benchmark datasets—Pima Indians Diabetes, Wisconsin Breast Cancer, and Heart Failure Prediction—the method achieved AUCs of 0.800, 0.984, and 0.919, respectively, and demonstrated reliable probability calibration through cross‑validated analysis and post‑hoc beta calibration.
arXiv Machine Learning
Aug 19
Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease
The study uses large-scale telehealth data and machine learning to classify self‑reported chronic kidney disease (CKD) status and identify key risk factors. A customized stacked ensemble model achieved balanced accuracy of 72.56–76.12% and AUROC of 79.59–82.29%. SHapley Additive exPlanations revealed that regular medical check‑ups, age, blood pressure, and mental health stress indicators are critical predictors of CKD.
arXiv Machine Learning
Aug 11
Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research
arXiv:2608. 07522v1 Announce Type: cross Abstract: We present a structured review of commonly used Explainable machine learning (XML) methodologies, including global and local interpretability tools such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots.
arXiv AI
Jul 1
Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantification
arXiv:2606. 30702v1 Announce Type: cross Abstract: Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demographic oversampling, and subgroup fairness.
arXiv Machine Learning
Sep 18
Machine-Learning Assessment of the Predictive Value of Inflammatory Biomarkers for Cognitive Impairment in an Older Hispanic Adult Cohort
The study evaluates whether inflammatory biomarkers can predict cognitive impairment in older Hispanic adults using interpretable machine learning on a small clinical dataset. A leakage‑safe Bernoulli/Categorical Naive Bayes model was trained on 165 participants from the Panama Aging Research Initiative, with continuous predictors discretized via supervised chi‑square and income treated categorically. The biomarker I‑309 (CCL1) emerged as the sole reliable incremental predictor, boosting ROC‑AUC from 0.630 to 0.740 and achieving statistically significant performance across repeated cross‑validation and random partitions.
arXiv Machine Learning
Jul 20
A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data
arXiv:2607. 15394v1 Announce Type: new Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility.
arXiv AI
Sep 3
General Demographic Pre-trained Models for Enhancing Predictive Performance Across Diseases and Population
The paper introduces the General Demographic Pre-trained (GDP) model, a lightweight foundation model that learns representations from the two most common clinical attributes—age and sex. By optimizing encoding and visit‑reordering strategies, GDP embeddings are shown to improve predictive performance when concatenated with raw features across various disease and geographic cohorts. The model outperforms several state‑of‑the‑art tabular foundation models and tree‑based algorithms, demonstrating that enriched demographic embeddings can enhance classification tasks while remaining fully compatible with standard classifiers.
arXiv Machine Learning
Jun 16
Beyond the Blood Draw: Explainable Machine Learning for Non-Invasive Dysglycemia Risk Screening
arXiv:2606. 16056v1 Announce Type: new Abstract: Dysglycemia, encompassing both prediabetes and diabetes, affects huge numbers of adults worldwide, yet many of them remain undiagnosed.
arXiv AI
Aug 5
SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology
arXiv:2608. 02803v1 Announce Type: cross Abstract: Attention-based multiple instance learning (ABMIL) is the predominant approach for slide-level prediction in computational pathology, yet its attention maps provide only local explanations: they indicate where a model focuses but not which histological features drive its predictions or how the model behaves across a patient cohort.