Year: 2026
Type: preprint
Abstract: Biomedical artificial intelligence (AI) increasingly relies on heterogeneous data sources, yet differences in data structure, terminology, evidence quality, and provenance complicate their integration into reliable knowledge resources. Here, we develop an evidence-aware framework for processing and integrating heterogeneous pharmacovigilance data from FDA Adverse Event Reporting System (FAERS) reports, biomedical literature, clinical trials, and electronic health records in Mass General Brigham. The framework combines large language model (LLM)-based information extraction, ontology-driven semantic normalization, cross-source evidence assessment, statistical and machine learning-based prioritization, and ingredient-based adverse drug event (ADE) relationship inference to transform heterogeneous evidence into a structured and evidence-based knowledge resource. The constructed knowledge system contains 11,588 drug nodes, 44,843 ingredient nodes, 3,275 ADE nodes, 993 indication nodes, and more than 2.47 million relationships, with each drug–ADE relationship assigned a confidence score. We further developed a drug-level web interface for accessing the knowledge resource and validated its utility in downstream AI tasks. Specifically, incorporating the developed knowledge resource improved the comprehensiveness, relevance, and overall quality of LLM-based drug-specific ADE synthesis and improved the precision and F1 score of clinical ADE relation extraction. These results demonstrate that AI-assisted processing combined with evidence-aware curation can transform heterogeneous biomedical evidence into reusable, AI-ready knowledge with explicit provenance and evidence strength information. The framework provides a general strategy for integrating heterogeneous biomedical data to support reproducible knowledge construction and downstream biomedical AI applications.
Source: medRxiv
Authors: Yiming Li, Huiyuan Zhou, Feng-Yu Yeh, Zenan Sun, Jie Zheng, Shayma Alzaidi, Hao Guan, Jianfu Li, Diane L. Seger, Liqin Wang, Manqi Li, Xingxian Li, Taiyang Chen, Joseph M. Plasek, Haifang Li, Aoran Jiang, Pengyu Hong, Fang Li, Wei Zhang, Licong Cui, David W Bates, Li Zhou, Yongqun He, Cui Tao
Institutions: Brigham and Women's Hospital, Harvard University, University of Michigan, Michigan Medicine, The University of Texas Health Science Center at Houston, Boston University, Massachusetts College of Pharmacy and Health Sciences, Mayo Clinic in Florida, Mass General Brigham, University of California, Berkeley, Brandeis University, Massachusetts General Hospital
Language: English
Subfield: Toxicology
Domain: Life Sciences
Open Access status: green
Awards: Dynamic learning for post-vaccine event prediction using temporal information in VAERS, Leveraging Longitudinal Data and Informatics Technology to Understand the Role of Bilingualism in Cognitive Resilience, Aging and Dementia, VIOLIN 2.0: Vaccine Information and Ontology LInked kNowledgebase
OpenAlex ID: W7223083584