Accurate risk stratification in patients with overweight or obesity is critical for guiding preventive care and allocating high-cost therapies such as GLP-1 receptor agonists. We present PatientTPP, a neural temporal point process (TPP) model trained on over 500,000 real-world clinical trajectories to learn patient representations from sequences of diagnoses, labs, and medications. We extend existing TPP modeling approaches to include static and numeric features and incorporate clinical knowledge for event encoding. PatientTPP representations support downstream prediction tasks, including classification of obesity-associated outcomes in low-risk individuals, even for events not explicitly modeled during training. In health economic evaluation, PatientTPP outperformed body mass index in stratifying patients by future cardiovascular-related healthcare costs, identifying higher-risk patients more efficiently. By modeling both the type and timing of clinical events, PatientTPP offers an interpretable, general-purpose foundation for patient risk modeling with direct applications to obesity-related care and cost targeting.
Population stratification for prediction of mortality in post-AKI patients
Institute of Applied Health Sciences, University of Aberdeen（阿伯丁大学应用卫生科学学院）；Institute of Mathematics and Statistics, University of Sao Paulo（圣保罗大学数学与统计学院）
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
Bruno Kessler Institute（布鲁诺·凯斯勒研究所）；University of Trento（特伦托大学）；Hasso Plattner Institute for Digital Health at Mount Sinai（西奈山哈索·普拉特纳数字健康研究所）；Institute for Next Generation Healthcare（下一代医疗保健研究所）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Azienda USL Centro “Santi”（圣蒂中央地方卫生局）；HK3 Lab（HK3实验室）
Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients
Harry Perkins Institute of Medical Research（哈里·珀金斯医学研究所）；University of Western Australia（西澳大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；Australian National University（澳大利亚国立大学）；University of Ottawa Heart Institute（渥太华大学心脏研究所）
Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation
The University of Western Australia（西澳大利亚大学）；Harry Perkins Institute of Medical Research（哈里·珀金斯医学研究所）；Sir Charles Gairdner Hospital（查尔斯·盖尔德纳爵士医院）；Murdoch University（默多克大学）；University of Ottawa Heart Institute（渥太华大学心脏研究所）；Fiona Stanley Hospital（菲奥娜·斯坦利医院）；University of Ottawa（渥太华大学）；Curtin University（科廷大学）
L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy naïve breast cancer patients
UnPaSt: unsupervised patient stratification by biclustering of omics data
University of Hamburg（汉堡大学）；Technical University of Munich（慕尼黑工业大学）；Netherlands Cancer Institute（荷兰癌症研究所）；ACMetric；University of Campinas（坎皮纳斯大学）；University of British Columbia（不列颠哥伦比亚大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔兰根-纽伦堡）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Altius Institute for Biomedical Sciences（Altius生物医学科学研究所）；University of Liège（列日大学）；Aarhus University（奥胡斯大学）；Aarhus University Hospital（奥胡斯大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Simon Fraser University（西蒙弗雷泽大学）；Vancouver Prostate Centre（温哥华前列腺中心）；Leibniz Institute of Virology（莱布尼茨病毒学研究所）；University of Southern Denmark（南丹麦大学）
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification
University of Michigan Medical School（密歇根大学医学院）；University of Michigan School of Nursing（密歇根大学护理学院）；University of Michigan（密歇根大学）；University of Michigan Health System（密歇根大学卫生系统）
Subgroup Performance Analysis in Hidden Stratifications
Inselspital, Bern University Hospital, University of Bern（伯尔尼大学医院因塞尔spital（伯尔尼大学医院））；Diabetes Center Berne（伯尔尼糖尿病中心）；Cluster of Excellence: Machine Learning - New Perspectives for Science, University of Tübingen（蒂宾根大学卓越集群：机器学习——科学新视角）；Faculty of Health Sciences and Medicine, University of Lucerne（卢塞恩大学健康科学与医学院）
Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers
Tallinn University of Technology (TalTech)（塔林理工大学）；Selfdiagnostics Deutschland GmbH（Selfdiagnostics德国公司）；Capitol Technology University（资本技术大学）；University of Tartu（塔尔图大学）
Integrate Any Omics: Towards genome-wide data integration for patient stratification
Peter Munk Cardiac Centre, University Health Network（彼得·蒙克心脏中心，大学健康网络）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Department of Molecular Genetics, University of Toronto（多伦多大学分子遗传学系）；Princess Margaret Cancer Centre, University Health Network（玛格丽特公主癌症中心，大学健康网络）；Department of Medical Biophysics, University of Toronto（多伦多大学医学生物物理学系）；Genetics and Genome Biology, the Hospital for Sick Children（病童医院遗传学与基因组生物学）
Federated unsupervised random forest for privacy-preserving patient stratification
Institute for Medical Informatics, Statistics and Documentation Medical University Graz（格拉茨医科大学医学信息学、统计学与文献学研究所）；Department of Pure and Applied Sciences, University of Urbino（乌尔比诺大学纯科学与应用科学系）
Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images
颈动脉粥样硬化是缺血性中风的主要原因，每年导致显著的死亡率和残疾率。早期诊断此类病例非常重要，因为它使临床医生能够应用更有效的治疗策略。本文介绍了一种可解释的颈动脉超声图像分类方法，用于颈动脉粥样硬化斑块患者的风险评估和分层。为了解决患者在有症状和无症状类别之间的高度不平衡分布（分别为16 vs 58），应用基于子采样方法的集成学习方案以及两阶段的成本敏感学习策略，该策略使用原始和重新采样的数据集。卷积神经网络（CNN）被用于建立系综的主要模型。使用六层深度CNN从图像中自动提取特征，然后进行两个完全连接层的分类阶段。所得结果（ROC曲线下面积（AUC）：73%，灵敏度：75%，特异性：70%）表明所提出的方法实现了可接受的区分性能。最后，在模型的预测上应用了可解释性方法，以揭示对模型决策过程的见解，并能够识别用于颈动脉粥样硬化斑块患者分层的新型图像生物标志物。可解释性方法与深度学习策略的整合可以促进识别用于颈动脉患者分层的新型超声图像生物标志物动脉粥样斑块
Detecting and explaining clinical-omics inconsistencies to improve patient cohort stratification: an application to Parkinson's disease
University of Murcia（穆尔西亚大学）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经学研究所）；UK Dementia Research Institute（英国痴呆症研究所）；University of Cambridge（剑桥大学）；Washington University School of Medicine（华盛顿大学医学院）
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
INSA-Lyon（里昂应用科学学院）；Université Claude Bernard Lyon 1（里昂克洛德·贝尔纳第一大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；University of Sherbrooke（舍布鲁克大学）；Sorbonne Université（索邦大学）；Hospices Civils de Lyon（里昂公立医院）
GraphBEAT: Graph-based Bucketized Event Aggregation Over Time for Early Risk Stratification of Gastrointestinal Cancers
Mayo Clinic（梅奥诊所）；Kern Center for the Science of Health Care Delivery（克恩医疗保健科学中心）；Department of Artificial Intelligence and Informatics, Mayo Clinic（梅奥诊所人工智能与信息学系）
CHM-Net: Center Heatmap-driven Macro-Micro Modeling Network for MRI-based Microbial Density Stratification
School of Computer Science and Engineering（计算机科学与工程学院）；School of Future Technology（未来技术学院）；Department of Medical Imaging（医学影像科）；Affiliated Cancer Hospital, Guangzhou Medical University（广州医学院附属癌症医院）
Risk Stratification for ICU Delirium using Pervasive Ambient Sensing Information
Department of Electrical and Computer Engineering, University of Florida, Gainesville, United States（佛罗里达大学电气与计算机工程系）；Department of Medicine, Stanford University, Stanford, United States（斯坦福大学医学系）；Department of Biomedical Engineering, University of Florida, Gainesville, United States（佛罗里达大学生物医学工程系）；Department of Medicine, University of Florida, Gainesville, United States（佛罗里达大学医学系）
Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia
University of Southern California（南加州大学）；Iran University of Medical Sciences（伊朗医科大学）；California State University, Long Beach（加州州立大学长滩分校）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）
IFRA: a machine learning-based Instrumented Fall Risk Assessment Scale derived from Instrumented Timed Up and Go test in stroke patients
Teseo Srl（特塞奥有限公司）；University of Genoa（热那亚大学）；CantoniLab（坎托尼实验室）；Need Institute, Foundation for the Cure and Rehabilitation of Neurological Diseases（神经疾病治疗与康复基金会尼德研究所）；Casa Di Cura Igea（伊 gea 疗养医院）
A dynamic risk score for early prediction of cardiogenic shock using machine learning
NYU Langone Health（纽约大学朗格尼医学中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Courant Institute of Mathematics, New York University（纽约大学库朗数学研究所）；New York University（纽约大学）
Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy(HCM-VAr-Risk Model)
University of Delaware（特拉华大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Taipei Veterans General Hospital（台北荣民总医院）；National Yang-Ming University（阳明大学）；University of California San Francisco（加利福尼亚大学旧金山分校）
Predicting High-Risk Colorectal Polyps in African Americans Using Pre-Colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation
Howard University College of Medicine（霍华德大学医学院）；University of Oslo（奥斯陆大学）；University College London Hospital（伦敦大学学院医院）
Deep Survival Analysis of Longitudinal EHR Data for Joint Prediction of Hospitalization and Death in COPD Patients
B2SLab, Institut de Recerca i Innovació en Salut (IRIS), Universitat Politècnica de Catalunya（B2SLab、创新与健康研究所（IRIS）、巴塞罗那理工大学）；Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络（CIBER-BBN））；Institut de Recerca Sant Joan de Déu（圣约翰德德乌研究所）；Evidenze Health；Servei de Vigilància Epidemiològica i Resposta a Emergències de Salut Pública a Barcelona Sud, Subdirecció Regional a Barcelona, Secretaria de Salut Pública, Departament de Salut, Generalitat de Catalunya（巴塞罗那南部公共卫生流行病监测与应急响应服务，巴塞罗那地区副秘书处，公共卫生秘书处，加泰罗尼亚自治区卫生部门）；Fundació Docència i Recerca Mútua Terrassa, Catalonia, Spain（特拉维撒互惠教育与研究基金会，加泰罗尼亚，西班牙）
Trajectory-guided discharge stratification for heart failure using short-context electronic health record sequence modeling
Sahlgrenska University Hospital（斯德哥尔摩大学医院）；Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg（计算机科学与工程系，查尔姆斯理工大学和乌普萨拉大学）；Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg（分子与临床医学系，斯德哥尔摩学院，乌普萨拉大学）；Department of Medicine, Geriatrics and Emergency Medicine, Sahlgrenska University Hospital（医学、老年医学和急诊医学系，斯德哥尔摩大学医院）；Department of Food and Nutrition, and Sport Science, Faculty of Education, University of Gothenburg（营养学与体育科学系，教育学院，乌普萨拉大学）；School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg（公共卫生与社区医学系，医学院，乌普萨拉大学）
