arXiv:2511.21561v1 Announce Type: new 
Abstract: This study proposes a risk prediction method based on a Multi-Scale Temporal Alignment Network (MSTAN) to address the challenges of temporal irregularity, sampling interval differences, and multi-scale dynamic dependencies in Electronic Health Records (EHR). The method focuses on temporal feature modeling by introducing a learnable temporal alignment mechanism and a multi-scale convolutional feature extraction structure to jointly model long-term trends and short-term fluctuations in EHR sequences. At the input level, the model maps multi-source clinical features into a unified high-dimensional semantic space and employs temporal embedding and alignment modules to dynamically weight irregularly sampled data, reducing the impact of temporal distribution differences on model performance. The multi-scale feature extraction module then captures key patterns across different temporal granularities through multi-layer convolution and hierarchical fusion, achieving a fine-grained representation of patient states. Finally, an attention-based aggregation mechanism integrates global temporal dependencies to generate individual-level risk representations for disease risk prediction and health status assessment. Experiments conducted on publicly available EHR datasets show that the proposed model outperforms mainstream baselines in accuracy, recall, precision, and F1-Score, demonstrating the effectiveness and robustness of multi-scale temporal alignment in complex medical time-series analysis. This study provides a new solution for intelligent representation of high-dimensional asynchronous medical sequences and offers important technical support for EHR-driven clinical risk prediction.

تقدم دراسة جديدة شبكة المحاذاة الزمنية متعددة المقاييس (MSTAN)، وهي نهج للتعلم الآلي مصمم لتحسين توقع المخاطر السريرية من خلال معالجة التحديات في السجلات الصحية الإلكترونية (EHR) مثل عدم انتظام الزمن واختلافات فترات العينة. تستخدم الطريقة آلية محاذاة زمنية قابلة للتعلم وبنية لاستخراج الميزات التلافيفية متعددة المقاييس لنمذجة الاتجاهات طويلة الأجل والتقلبات قصيرة الأجل في بيانات EHR بشكل فعال.

Un nuevo estudio presenta la Red de Alineación Temporal Multi-escala (MSTAN), un enfoque de aprendizaje automático diseñado para mejorar la predicción de riesgos clínicos al abordar desafíos en los Registros Electrónicos de Salud (EHR), como la irregularidad temporal y las diferencias en los intervalos de muestreo. El método emplea un mecanismo de alineación temporal aprendible y una estructura de extracción de características convolucionales multi-escala para modelar eficazmente tanto las tendencias a largo plazo como las fluctuaciones a corto plazo en los datos de EHR.

Une nouvelle étude présente le Multi-Scale Temporal Alignment Network (MSTAN), une approche d'apprentissage automatique conçue pour améliorer la prédiction des risques cliniques en s'attaquant aux défis des dossiers de santé électroniques (DSE), tels que l'irrégularité temporelle et les variations d'intervalle d'échantillonnage. La méthode utilise un mécanisme d'alignement temporel apprenable et une structure d'extraction de caractéristiques convolutionnelles multi-échelles pour modéliser efficacement à la fois les tendances à long terme et les fluctuations à court terme dans les données DSE.

A new study introduces the Multi-Scale Temporal Alignment Network (MSTAN), a machine learning approach designed to enhance clinical risk prediction by addressing challenges in Electronic Health Records (EHR) such as temporal irregularity and varying sampling intervals. The method employs a learnable temporal alignment mechanism and a multi-scale convolutional feature extraction structure to effectively model both long-term trends and short-term fluctuations in EHR data.

Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Records

arXiv:2512.06652v1 Announce Type: new 
Abstract: Accurate prediction of the need for invasive mechanical ventilation (IMV) in intensive care units (ICUs) patients is crucial for timely interventions and resource allocation. However, variability in patient populations, clinical practices, and electronic health record (EHR) systems across institutions introduces domain shifts that degrade the generalization performance of predictive models during deployment. Test-Time Training (TTT) has emerged as a promising approach to mitigate such shifts by adapting models dynamically during inference without requiring labeled target-domain data. In this work, we introduce Adaptive Test-Time Training (AdaTTT), an enhanced TTT framework tailored for EHR-based IMV prediction in ICU settings. We begin by deriving information-theoretic bounds on the test-time prediction error and demonstrate that it is constrained by the uncertainty between the main and auxiliary tasks. To enhance their alignment, we introduce a self-supervised learning framework with pretext tasks: reconstruction and masked feature modeling optimized through a dynamic masking strategy that emphasizes features critical to the main task. Additionally, to improve robustness against domain shifts, we incorporate prototype learning and employ Partial Optimal Transport (POT) for flexible, partial feature alignment while maintaining clinically meaningful patient representations. Experiments across multi-center ICU cohorts demonstrate competitive classification performance on different test-time adaptation benchmarks.

تم تقديم إطار عمل جديد يسمى Adaptive Test-Time Training (AdaTTT) لتحسين التنبؤ بالحاجة إلى التهوية الميكانيكية الغازية (IMV) لدى مرضى وحدات العناية المركزة. تتناول هذه الطريقة التحديات التي تطرحها التباينات في مجموعات المرضى والممارسات السريرية عبر مؤسسات مختلفة، مما قد يعيق فعالية النماذج التنبؤية أثناء نشرها.

Se ha introducido un nuevo marco llamado Adaptive Test-Time Training (AdaTTT) para mejorar la predicción de la necesidad de ventilación mecánica invasiva (IMV) en pacientes de UCI. Este enfoque aborda los desafíos que plantea la variabilidad en las poblaciones de pacientes y las prácticas clínicas en diferentes instituciones, lo que puede obstaculizar la efectividad de los modelos predictivos durante su implementación.

Un nouveau cadre appelé Adaptive Test-Time Training (AdaTTT) a été introduit pour améliorer la prédiction des besoins en ventilation mécanique invasive (IMV) chez les patients en soins intensifs. Cette approche traite les défis posés par la variabilité des populations de patients et des pratiques cliniques entre différentes institutions, ce qui peut entraver l'efficacité des modèles prédictifs lors de leur déploiement.

A new framework called Adaptive Test-Time Training (AdaTTT) has been introduced to improve the prediction of invasive mechanical ventilation (IMV) needs in ICU patients. This approach addresses the challenges posed by variability in patient populations and clinical practices across different institutions, which can hinder the effectiveness of predictive models during deployment.

Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Records

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