Identifying Suicidal Adolescents from Mental Health Records Using Natural Language Processing

TY - JOUR

T1 - Identifying Suicidal Adolescents from Mental Health Records Using Natural Language Processing

AU - Velupillai, Sumithra

AU - Epstein, Sophie

AU - Bittar, André

AU - Stephenson, Thomas

AU - Dutta, Rina

AU - Downs, Johnny

PY - 2019/8/21

Y1 - 2019/8/21

N2 - Suicidal ideation is a risk factor for self-harm, completed suicide and can be indicative of mental health issues. Adolescents are a particularly vulnerable group, but few studies have examined suicidal behaviour prevalence in large cohorts. Electronic Health Records (EHRs) are a rich source of secondary health care data that could be used to estimate prevalence. Most EHR documentation related to suicide risk is written in free text, thus requiring Natural Language Processing (NLP) approaches. We adapted and evaluated a simple lexicon- and rule-based NLP approach to identify suicidal adolescents from a large EHR database. We developed a comprehensive manually annotated EHR reference standard and assessed NLP performance at both document and patient level on data from 200 patients ( 5000 documents). We achieved promising results (>80% f1 score at both document and patient level). Simple NLP approaches can be successfully used to identify patients who exhibit suicidal risk behaviour, and our proposed approach could be useful for other populations and settings.

AB - Suicidal ideation is a risk factor for self-harm, completed suicide and can be indicative of mental health issues. Adolescents are a particularly vulnerable group, but few studies have examined suicidal behaviour prevalence in large cohorts. Electronic Health Records (EHRs) are a rich source of secondary health care data that could be used to estimate prevalence. Most EHR documentation related to suicide risk is written in free text, thus requiring Natural Language Processing (NLP) approaches. We adapted and evaluated a simple lexicon- and rule-based NLP approach to identify suicidal adolescents from a large EHR database. We developed a comprehensive manually annotated EHR reference standard and assessed NLP performance at both document and patient level on data from 200 patients ( 5000 documents). We achieved promising results (>80% f1 score at both document and patient level). Simple NLP approaches can be successfully used to identify patients who exhibit suicidal risk behaviour, and our proposed approach could be useful for other populations and settings.

U2 - 10.3233/SHTI190254

DO - 10.3233/SHTI190254

M3 - Article

C2 - 31437956

SN - 0926-9630

VL - 264

SP - 413

EP - 417

JO - STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS

JF - STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS

ER -