Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing

BMC Medical Informatics and Decision Making
Qiu-Yue ZhongMichelle A Williams

Abstract

We examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening suicidal behavior among pregnant women in electronic medical records (EMRs). Women aged 10-64 years with at least one diagnostic code related to pregnancy or delivery (N = 275,843) from Partners HealthCare were included as our "datamart." Diagnostic codes related to suicidal behavior were applied to the datamart to screen women for suicidal behavior. Among women without any diagnostic codes related to suicidal behavior (n = 273,410), 5880 women were randomly sampled, of whom 1120 had at least one mention of terms related to suicidal behavior in clinical notes. NLP was then used to process clinical notes for the 1120 women. Chart reviews were performed for subsamples of women. Using diagnostic codes, 196 pregnant women were screened positive for suicidal behavior, among whom 149 (76%) had confirmed suicidal behavior by chart review. Using NLP among those without diagnostic codes, 486 pregnant women were screened positive for suicidal behavior, among whom 146 (30%) had confirmed suicidal behavior by chart review. The use of NLP substantially improves the sensitivity of screening suicidal...Continue Reading

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Citations

Feb 23, 2019·Paediatric and Perinatal Epidemiology·Qiu-Yue ZhongMichelle A Williams
Apr 16, 2020·BMC Medical Informatics and Decision Making·Lingling ZhouHao Huang
May 8, 2020·Archives of Suicide Research : Official Journal of the International Academy for Suicide Research·Jennifer M BoggsArne Beck
Aug 23, 2020·International Journal of Environmental Research and Public Health·Rebecca A BernertFreddy Abnousi
Feb 19, 2021·Journal of Medical Internet Research·Isaac S KohaneTianxi Cai
Nov 26, 2021·Health Informatics Journal·Hasan ZafariAlexander Signer

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Software Mentioned

DocTimeRel
Medical Language Extraction and Encoding System ( MedLEE )
cTAKES

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