Integrating Multi-omics Data with EHR for Precision Medicine Using Advanced Artificial Intelligence

• Published in: IEEE reviews in biomedical engineering Vol. 17; pp. 1 - 15
• Main Authors: Tong, Li, Shi, Wenqi, Isgut, Monica, Zhong, Yishan, Lais, Peter, Gloster, Logan, Sun, Jimin, Swain, Aniketh, Giuste, Felipe, Wang, May D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial Intelligence; Bioinformatics; Biological analysis; Biomedical data; Cancer; data integration; Delivery of Health Care; Dimensionality reduction; Diseases; Electronic Health Records; Electronic medical records; Feature extraction; Humans; Medical services; Medicine; Multi-omics; Multiomics; Next-generation sequencing; precision; Precision Medicine; Wearable technology
• ISSN: 1937-3333; 1941-1189; 1941-1189
• DOI: 10.1109/RBME.2023.3324264

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 reality. However, due to data volume and 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 development in integrating multi-omics data with EHRs for precision medicine.