Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence

作者：Tong Li, Wenqi Shi, Monica Isgut, Yishan Zhong, Peter Lais, Logan Gloster, Jimin Sun, Aniketh Swain, Felipe Giuste, May D. Wang · 发表于：IEEE Reviews in Biomedical Engineering · 年份：2023 · DOI：10.1109/rbme.2023.3324264 · 被引用次数：115 · 研究领域：Gene expression and cancer classification、Bioinformatics and Genomic Networks、Machine Learning in Healthcare

With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.