Technical Name Deep Heterogeneous Multimodal Learning Techniques for Predicting Hospital Readmission and Mortality Risk on Heart Failure Patients
Project Operator National Yang Ming Chiao Tung University / Taipei Veterans General Hospital
Project Host 曾新穆
Summary
We developed a Deep Heterogeneous Multimodal Learning-Based Technique for Predicting Hospital Readmission and Mortality Risk in heart failure patients. This technique integrates heterogeneous modalities, including clinical data, electrocardiograms, and chest X-rays, to predict short-term and long-term risks for mortality and readmission. The relevant clinical application of this technique has been recognized by the 20th National Innovation Award and under validation in multiple hospitals.
Scientific Breakthrough
Our Deep Heterogeneous Multimodal Learning-Based Technique extracts features from diverse clinical modalities, fuses them via the heterogeneous representation fusion technique and the weighting strategy, and constructs a stacked ensemble classifier to predict short-term and long-term risks for readmission and mortality in heart failure patients. It surpasses traditional and frontier methods, achieving predictive performance with AUCs above 0.8.
Industrial Applicability
Heart failure prevalence has been rising in increased aged populations and become one of the main factors of hospitalizations for patients aged 65 and older. Due to high readmission and mortality rates, accurate risk prediction tools are under high demands. Our technique fuses heterogeneous modalities to predict short-term and long-term risks for readmission and mortality in heart failure patients, carring high application values in smart medicine areas, including effective clinical decision support, cost reduction and improved outcomes on chronic disease care.
Keyword Multimodal Learning Artificial Intelligence Heart Failure Heterogeneous Modalities Readmission Prediction Mortality Prediction Smart Healthcare Electrocardiograms Chest X-rays Clinical Data
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  • Chao Ling Shen