TY - JOUR
T1 - Recommended practices and ethical considerations for natural language processing-assisted observational research
T2 - A scoping review
AU - Fu, Sunyang
AU - Wang, Liwei
AU - Moon, Sungrim
AU - Zong, Nansu
AU - He, Huan
AU - Pejaver, Vikas
AU - Relevo, Rose
AU - Walden, Anita
AU - Haendel, Melissa
AU - Chute, Christopher G.
AU - Liu, Hongfang
N1 - Funding Information:
This study was funded by NIH‐NCATS‐U01TR002062.
Funding Information:
This work was made possible by National Institute of Health (NIH) grant number U01TR002062. We also want to acknowledge the OHNLP Consortium, National Center for Data to Health (CD2H), and iEC text analytics community.
Publisher Copyright:
© 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85145170122&partnerID=8YFLogxK
U2 - 10.1111/cts.13463
DO - 10.1111/cts.13463
M3 - Review article
C2 - 36478394
AN - SCOPUS:85145170122
SN - 1752-8054
VL - 16
SP - 398
EP - 411
JO - Clinical and Translational Science
JF - Clinical and Translational Science
IS - 3
ER -