The era of big data has made vast amounts of clinical data readily available, particularly in the form of electronic health records (EHRs), which provides unprecedented opportunities for developing data-driven diagnostic tools to enhance clinical decision making. However, the application of EHRs in data-driven modeling faces challenges such as irregularly spaced multi-variate time series, issues of incompleteness, and data imbalance. Realizing the full data potential of EHRs hinges on the development of advanced analytical models. In this paper, we propose a novel Missingness-aware mUlti-branching Self-Attention Encoder (MUSE-Net) to cope with the challenges in modeling longitudinal EHRs for data-driven disease prediction. The proposed MUSE-Net is composed by four novel modules including: (1) a multi-task Gaussian process (MGP) with missing value masks for data imputation; (2) a multi-branching architecture to address the data imbalance problem; (3) a time-aware self-attention encoder to account for the irregularly spaced time interval in longitudinal EHRs; (4) interpretable multi-head attention mechanism that provides insights into the importance of different time points in disease prediction, allowing clinicians to trace model decisions. We evaluate the proposed MUSE-Net using both synthetic and real-world datasets. Experimental results show that our MUSE-Net outperforms existing methods that are widely used to investigate longitudinal signals.
Mind the Missing: Variable-Aware Representation Learning for Irregular EHR Time Series using Large Language Models
School of Industrial Management Engineering, Korea University（韩国大学工业管理工程学院）；Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Pittsburgh（匹兹堡大学医学部呼吸科、过敏科、重症医学科及睡眠医学科）
End to End Autoencoder MLP Framework for Sepsis Prediction
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；State Key Laboratory of Public Big Data, Guizhou University（贵州大学公共大数据国家重点实验室）
Off-Policy Evaluation for Missingness-Aware Policies in MDPs with Rewards Missing Not at Random
Department of Statistics, University of Michigan at Ann Arbor（密歇根大学安娜堡分校统计学系）；Department of Statistics（统计学系）；Applied Probability, University of California at Santa Barbara（加州大学圣巴巴拉分校应用概率系）；Department of Mathematical Sciences, University of Texas at Dallas（德克萨斯大学达拉斯分校数学科学系）
DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China（香港大学电子与电气工程系）；Shenzhen MSU-BIT University, China（深圳MSU-BIT大学）；Department of Data Science, City University of Hong Kong, China（香港城市大学数据科学系）；Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence, Macao Polytechnic University（澳门理工大学机器翻译与人工智能应用技术研究中心）
FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
Medical & Imaging Informatics (MII) Group, University of California, Los Angeles (UCLA), CA, USA（加州大学洛杉矶分校医学与影像信息学组）；Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea（韩国蔚山科学技术院工业工程系）；Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea（韩国蔚山科学技术院人工智能研究生院）
Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
Key Laboratory of Big Data \& Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education School of Computer Science；Technology, Beijing Jiaotong University Beijing China；School of Computer Science；Tangshan Research Institute, Beijing Jiaotong University Tangshan China；Key Laboratory of Big Data \& Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education；Technology, Beijing Jiaotong University；Tangshan Research Institute, Beijing Jiaotong University
PROMISE-AD: Progression-aware Multi-horizon Survival Estimation for Alzheimer's Disease Progression and Dynamic Tracking
Department of Radiology and Biomedical Imaging, Yale School of Medicine（放射科与生物医学成像系，耶鲁医学院）；Department of Radiology Oncology, Wake Forest University School of Medicine（放射肿瘤科，沃杰福大学医学院）；Department of Applied Mathematics & Statistics and Department of Computer Science, Stony Brook University（应用数学与统计系和计算机科学系，史泰文布鲁克大学）
