An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges.
To address these issues, the presenter worked to conduct a scoping review of NLP-assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through their investigation, his team discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, Fu will outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health.
Presenters
Sunyang Fu is an Assistant Professor at McWilliams School of Biomedical Informatics and the Associate Director of Team Science at the Center of Translational AI Excellence and Applications in Medicine (TEAM-AI) at UTHealth Houston. Prior to joining UTHealth Houston, Dr. Fu worked as a biomedical informatician and data scientist in the Department of AI and Informatics at Mayo Clinic. His research focuses on clinical research informatics and clinical and translational research, with an emphasis on accelerating, improving, and governing the secondary use of Electronic Health Records (EHRs) for high throughput, reproducible, fair, and trustworthy discoveries.