Multimodal risk prediction with physiological signals, medical images and clinical notes.

ABSTRACT: The broad adoption of electronic health record (EHR) systems brings us a tremendous amount of clinical data and thus provides opportunities to conduct data-based healthcare research to solve various clinical problems in the medical domain. Machine learning and deep learning methods are widely used in the medical informatics and healthcare domain due to their power to mine insights from raw data. When adapting deep learning models for EHR data, it is essential to consider its heterogeneous nature: EHR contains patient records from various sources including medical tests (e.g. blood test, microbiology test), medical imaging, diagnosis, medications, procedures, clinical notes, etc. Those modalities together provide a holistic view of patient health status and complement each other. Therefore,

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10918115 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature