Med-PRSIMD: Enhanced Complex Disease Risk Prediction through Integrative Analysis of Multi-Type Data and Medical History Records

Abstract

Predicting complex diseases using computational methods have rapidly evolved, which accelerates precision medicine, disease prevention, and so on. In past years, especially after emergence of genomics, major focus was inclined connecting genetic influence to complex diseases wherein many other essential factors such as environment, lifestyles, and so on were constantly neglected. In recent years, researchers have begun predicting disease risk scores by integrating genetic variants data and different lifestyle factors (alcohol intake, etc) and physical parameters (body mass index, age, etc). However, a potential missing link still exists there as most of those methods did not adequately address how to integrate the information from the medical history record that plays a crucial role in complex disease occurrence. In this paper, we have proposed Med-PRSIMD model that integrates genetic variants, lifestyle factors, physical attributes, and medical history records to enhance disease risk prediction. Our approach employs advanced machine learning techniques to capture complex interactions among these diverse data sources, providing more comprehensive and accurate risk assessments for complex diseases.

Yu Xu 许煜

Ph.D. Candidate

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