arXiv:2511.17629v1 Announce Type: new 
Abstract: We study classification under extreme class imbalance where recall and calibration are both critical, for example in medical diagnosis scenarios. We propose AF-SMOTE, a mathematically motivated augmentation framework that first synthesizes minority points and then filters them by an adversarial discriminator and a boundary utility model. We prove that, under mild assumptions on the decision boundary smoothness and class-conditional densities, our filtering step monotonically improves a surrogate of F_beta (for beta >= 1) while not inflating Brier score. On MIMIC-IV proxy label prediction and canonical fraud detection benchmarks, AF-SMOTE attains higher recall and average precision than strong oversampling baselines (SMOTE, ADASYN, Borderline-SMOTE, SVM-SMOTE), and yields the best calibration. We further validate these gains across multiple additional datasets beyond MIMIC-IV. Our successful application of AF-SMOTE to a healthcare dataset using a proxy label demonstrates in a disease-agnostic way its practical value in clinical situations, where missing true positive cases in rare diseases can have severe consequences.

تقدم دراسة جديدة إطار AF-SMOTE، وهو إطار مبتكر مصمم لتحسين دقة التصنيف في السيناريوهات التي تتميز بعدم توازن شديد في الفئات، وخاصة في التشخيصات الطبية. تقوم هذه الطريقة بتوليد نقاط بيانات أقلية وتستخدم عملية تصفية عدائية لتحسين مقاييس الاسترجاع والمعايرة.

Un nuevo estudio presenta AF-SMOTE, un marco innovador diseñado para mejorar la precisión de la clasificación en escenarios caracterizados por un desequilibrio extremo de clases, especialmente en diagnósticos médicos. Este método sintetiza puntos de datos minoritarios y emplea un proceso de filtrado adversarial para mejorar las métricas de recuperación y calibración.

Une nouvelle étude présente AF-SMOTE, un cadre innovant conçu pour améliorer la précision de la classification dans des scénarios caractérisés par un déséquilibre extrême des classes, en particulier dans les diagnostics médicaux. Cette méthode synthétise des points de données minoritaires et utilise un processus de filtrage adversarial pour améliorer les métriques de rappel et de calibration.

A new study introduces AF-SMOTE, an innovative framework designed to enhance classification accuracy in scenarios characterized by extreme class imbalance, particularly in medical diagnostics. This method synthesizes minority data points and employs an adversarial filtering process to improve recall and calibration metrics.

Boundary-Aware Adversarial Filtering for Reliable Diagnosis under Extreme Class Imbalance

arXiv:2512.08012v1 Announce Type: new 
Abstract: In critical care settings such as the Intensive Care Unit, clinicians face the complex challenge of balancing conflicting objectives, primarily maximizing patient survival while minimizing resource utilization (e.g., length of stay). Single-objective Reinforcement Learning approaches typically address this by optimizing a fixed scalarized reward function, resulting in rigid policies that fail to adapt to varying clinical priorities. Multi-objective Reinforcement Learning (MORL) offers a solution by learning a set of optimal policies along the Pareto Frontier, allowing for dynamic preference selection at test time. However, applying MORL in healthcare necessitates strict offline learning from historical data.
  In this paper, we benchmark three offline MORL algorithms, Conditioned Conservative Pareto Q-Learning (CPQL), Adaptive CPQL, and a modified Pareto Efficient Decision Agent (PEDA) Decision Transformer (PEDA DT), against three scalarized single-objective baselines (BC, CQL, and DDQN) on the MIMIC-IV dataset. Using Off-Policy Evaluation (OPE) metrics, we demonstrate that PEDA DT algorithm offers superior flexibility compared to static scalarized baselines. Notably, our results extend previous findings on single-objective Decision Transformers in healthcare, confirming that sequence modeling architectures remain robust and effective when scaled to multi-objective conditioned generation. These findings suggest that offline MORL is a promising framework for enabling personalized, adjustable decision-making in critical care without the need for retraining.

دراسة حديثة تقيم ثلاثة خوارزميات للتعلم المعزز متعدد الأهداف (MORL) خارج الخط: Conditioned Conservative Pareto Q-Learning وAdaptive CPQL ووكيل القرار Pareto الفعال المعدل، في بيئات الرعاية الحرجة، وخاصة في وحدة العناية المركزة. تهدف هذه الأبحاث إلى معالجة تعقيدات تحقيق التوازن بين بقاء المرضى واستخدام الموارد من خلال التكيف الديناميكي للسياسات بناءً على البيانات التاريخية.

Un estudio reciente evalúa tres algoritmos de Aprendizaje por Refuerzo Multi-Objetivo (MORL) fuera de línea: Conditioned Conservative Pareto Q-Learning, Adaptive CPQL y un Agente de Decisión Pareto Eficiente modificado, en entornos de cuidados críticos, especialmente en la Unidad de Cuidados Intensivos. Esta investigación busca abordar las complejidades de equilibrar la supervivencia del paciente con la utilización de recursos mediante la adaptación dinámica de políticas basada en datos históricos.

Une étude récente évalue trois algorithmes de renforcement multi-objectifs hors ligne (MORL) : Conditioned Conservative Pareto Q-Learning, Adaptive CPQL et un agent de décision modifié Pareto Efficient Decision Transformer, dans des contextes de soins critiques, notamment l'unité de soins intensifs. Cette recherche vise à résoudre les complexités de l'équilibre entre la survie des patients et l'utilisation des ressources grâce à une adaptation dynamique des politiques basée sur des données historiques.

A recent study benchmarks three offline Multi-Objective Reinforcement Learning (MORL) algorithms—Conditioned Conservative Pareto Q-Learning, Adaptive CPQL, and a modified Pareto Efficient Decision Agent Decision Transformer—in critical care settings, particularly the Intensive Care Unit. This research aims to address the complexities of balancing patient survival with resource utilization through dynamic policy adaptation based on historical data.

Benchmarking Offline Multi-Objective Reinforcement Learning in Critical Care

arXiv:2512.07856v1 Announce Type: new 
Abstract: Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive medical testing and considerable costs, making it impractical to perform all possible medical tests on a patient to diagnose or predict hundreds or thousands of diseases. In this work, we propose Collaborative Learning for Disease Detection (CLDD), a novel graph-based deep learning model that formulates disease detection as a collaborative learning task by exploiting associations among diseases and similarities among patients adaptively. CLDD integrates patient-disease interactions and demographic features from electronic health records to detect hundreds or thousands of diseases for every patient, with little to no reliance on the corresponding medical tests. Extensive experiments on a processed version of the MIMIC-IV dataset comprising 61,191 patients and 2,000 diseases demonstrate that CLDD consistently outperforms representative baselines across multiple metrics, achieving a 6.33\% improvement in recall and 7.63\% improvement in precision. Furthermore, case studies on individual patients illustrate that CLDD can successfully recover masked diseases within its top-ranked predictions, demonstrating both interpretability and reliability in disease prediction. By reducing diagnostic costs and improving accessibility, CLDD holds promise for large-scale disease screening and social health security.

تم تقديم نهج جديد يسمى Collaborative Learning for Disease Detection (CLDD)، والذي يستخدم نموذج تعلم عميق قائم على الرسوم البيانية للكشف عن الأمراض دون الحاجة إلى اختبارات طبية موسعة. تستفيد هذه الطريقة من التفاعلات بين المرضى والأمراض والبيانات الديموغرافية من السجلات الصحية الإلكترونية، بهدف تحديد مجموعة واسعة من الأمراض بكفاءة.

Se ha introducido un nuevo enfoque llamado Collaborative Learning for Disease Detection (CLDD), que utiliza un modelo de aprendizaje profundo basado en grafos para detectar enfermedades sin pruebas médicas extensas. Este método aprovecha las interacciones entre pacientes y enfermedades, así como los datos demográficos de los registros de salud electrónicos, con el objetivo de identificar eficientemente una amplia gama de enfermedades.

Une nouvelle approche appelée Collaborative Learning for Disease Detection (CLDD) a été introduite, utilisant un modèle d'apprentissage profond basé sur des graphes pour détecter des maladies sans tests médicaux étendus. Cette méthode exploite les interactions patient-maladie et les données démographiques des dossiers de santé électroniques, visant à identifier efficacement un large éventail de maladies.

A novel approach called Collaborative Learning for Disease Detection (CLDD) has been introduced, utilizing a graph-based deep learning model to detect diseases without extensive medical testing. This method leverages patient-disease interactions and demographic data from electronic health records, aiming to identify a wide range of diseases efficiently.

Boundary-Aware Adversarial Filtering for Reliable Diagnosis under Extreme Class Imbalance

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