Causal Transformer for Learning Embeddings from Structured Medical History Records and Multi-Source Data Integration for Complex Disease Risk Prediction

Abstract

Traditional disease risk prediction models predominantly rely on statistical algorithms and often focus on genetic factors or a limited set of lifestyle factors to estimate the risk of disease onset. Recently, more comprehensive approaches have emerged that integrate genetic factors with additional lifestyle factors (e.g., alcohol intake) and physical features (e.g., body mass index, age) to increase predictive accuracy. Since the onset of complex diseases is often accompanied by the occurrence of comorbidities, incorporating medical history records is a critical yet underexplored avenue for improving risk prediction. In this study, we propose a novel framework, MIDRP (Multi-source Integration for Disease Risk Prediction), which incorporates genetic variants, lifestyle factors, physical attributes, and medical history records to achieve more robust and accurate predictions. At the heart of our approach lies a causal Transformer model that learns embeddings from structured medical history records while capturing temporal dependencies and causal relationships between diseases. We systematically evaluated MIDRP on multiple complex diseases and demonstrated its superior performance compared to existing risk prediction methods, highlighting the importance of integrating diverse data sources for comprehensive disease risk assessment.

Yu Xu 许煜

Ph.D. Candidate

My research interests include computational metabolomics, computational genomics, and large language models in metabolomics.