Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review

Sunyang Fu, Liwei Wang, Sungrim Moon, Nansu Zong, Huan He, Vikas Pejaver, Rose Relevo, Anita Walden, Melissa Haendel, Christopher G. Chute, Hongfang Liu

Research output: Contribution to journalReview articlepeer-review

Abstract

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, we conducted a scoping review of NLP-assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we 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, we 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.

Original languageEnglish (US)
JournalClinical and translational science
DOIs
StateAccepted/In press - 2022

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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