Opioid Use Disorder (OUD) is a public health crisis costing the US billions of dollars annually in healthcare, lost workplace productivity, and crime. Analyzing longitudinal healthcare data is critical in addressing many real-world problems in healthcare. Leveraging the real-world longitudinal healthcare data, we propose a novel multi-stream transformer model called MUPOD for OUD identification. MUPOD is designed to simultaneously analyze multiple types of healthcare data streams, such as medications and diagnoses, by attending to segments within and across these data streams. Our model tested on the data from 392,492 patients with long-term back pain problems showed significantly better performance than the traditional models and recently developed deep learning models.
Explainability of Traditional and Deep Learning Models on Longitudinal Healthcare Records
Amazon ML Solutions Lab（亚马逊机器学习解决方案实验室）；Merck & Co., Inc.（默沙东公司）
深度学习的最新进展已经引起了对在纵向医疗保健记录上训练深度学习模型以预测一系列医疗事件的兴趣，其中模型展示出高预测性能。然而，预测性能是必要的，但还不够，需要模型的解释和推理来说服临床医生持续使用。对可解释性的严格评估往往是缺失的，因为模型（传统与深度）和各种可解释性方法之间的比较尚未得到充分研究。此外，根据临床医生的观点，评估可解释性所需的基本事实可能是高度主观的。我们的工作是第一个在纵向医疗保健数据的全局和个体预测水平上评估传统（XGBoost）和深度学习（LSTM with Attention）模型之间和内部的可解释性性能的工作之一。我们使用三种流行的方法比较了可解释性：1）SHapley加法解释（SHAP），2）分层相关传播（LRP）和3）注意力。这些实现被应用于合成生成的数据集与设计的地面实况和现实世界的医疗保险索赔数据集。我们发现，总体而言，与XGBoost相比，SHAP或LRP的LSTM在全局和局部水平上都提供了更好的可解释性，而点积注意力的LSTM未能产生合理的结果。随着医疗保健数据量的爆炸式增长和深度学习的进步，评估可解释性的需求将是在医疗保健环境中成功采用深度学习模型的关键。
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
Georgia Institute of Technology（佐治亚理工学院）；IBM Research（IBM研究院）；Vanderbilt University（范德堡大学）；Sutter Health（萨特健康）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare
School of Computer Science, McGill University（麦吉尔大学计算机学院）；School of Population and Global Health, McGill University（麦吉尔大学人口与全球健康学院）；Mila Quebec AI institute（米拉魁北克人工智能研究所）
提出 TimelyGPT，通过 xPos、循环注意力与时间卷积实现可外推时间序列预训练，在连续生物信号和不规则 EHR 数据上提升长期预测表现。
A Temporal Machine Learning-Based Time-to-Event Model for Predicting ALS Progression and Healthcare Utilization
Harrison College of Pharmacy, Auburn University（奥本大学哈里森药学院）；Fisk University（菲斯克大学）；Lewis Katz School of Medicine, Temple University（天普大学刘易斯·卡茨医学院）；Emory University（埃默里大学）；Barnett College of Public Health, Temple University（天普大学巴尼特公共卫生学院）
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Technical University of Denmark（丹麦技术大学）；KTH Stockholm（瑞典皇家理工学院）；CIFAR Azrieli Global Scholar（CIFAR Azrieli 全球学者计划）；GlaxoSmithKline（葛兰素史克）
ISTHMUS: Secure, Scalable, Real-time and Robust Machine Learning Platform for Healthcare
Parkland Center for Clinical Innovation（帕克兰临床创新中心）；Dallas, TX 75247, USA（美国得克萨斯州达拉斯市75247（注：此条为地址，非机构，按要求不应提取，实际仅需保留Parkland Center for Clinical Innovation））
Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Centre for Medical Informatics, University of Edinburgh（爱丁堡大学医学信息学中心）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；Health Data Research UK（英国健康数据研究中心）
Patient Similarity Analysis with Longitudinal Health Data
University of Zurich（苏黎世大学）；University Hospital of Zurich（苏黎世大学医院）；University of Applied Sciences and Arts Northwestern Switzerland（西北瑞士应用科学与艺术大学）；Yale University School of Medicine（耶鲁大学医学院）
A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, 1 Canada（儿童健康评估科学计划，多伦多医院儿童医学中心，多伦多，加拿大）；Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA, USA（斯坦福大学生物医学信息学研究中心，斯坦福大学，帕洛阿尔托，美国）；Universidad del Norte, Barranquilla, Columbia（北方大学，巴兰基利亚，哥伦比亚）；Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, Canada（血液肿瘤科，多伦多医院儿童医学中心，多伦多，加拿大）
Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies
Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics（过渡人工智能研究组，数学与统计学学院）；Centre for Artificial Intelligence and Innovation（人工智能与创新中心）；Pingla Institute（Pingla研究所）
Comparative analysis of unsupervised clustering techniques using validation metrics: Study on cognitive features from the Canadian Longitudinal Study on Aging (CLSA)
HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning
University of Delaware Newark Delaware USA；Thomas Jefferson University \& Nemours Children's Health Wilmington Delaware USA；University of Delaware；Thomas Jefferson University \& Nemours Children's Health
