The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records. This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications. However, the problem is very challenging since patients' history records contain multiple heterogeneous temporal events such as lab tests, diagnosis, and drug administrations. The visiting patterns of different types of events vary significantly, and there exist complex nonlinear relationships between different events. In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of our proposed approach over competitive baselines.
Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models
Eidgenössische Technische Hochschule (ETH), Zürich, Switzerland（瑞士苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems, Tübingen, Germany（德国图宾根智能系统研究所）；ELLIS Institute, Tübingen, Germany（德国图宾根ELLIS研究所）；Stanford School of Medicine, CA, USA（美国斯坦福医学院）
DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits
Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Hyderabad Campus（计算机科学与信息系统系，比拉理工学院，比拉理工学院，海得拉巴校区）
Deep neuroevolution for limited, heterogeneous data: proof-of-concept application to Neuroblastoma brain metastasis using a small virtual pooled image collection
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
