Open AccessComparative Analysis of Patient Distress in Opioid Treatment Programs using Natural Language Processing
Author(s) -
Fatemeh Shah-Mohammadi,
Wanting Cui,
Keren Bachi,
Yasmin L. Hurd,
Joseph Finkelstein
Publication year - 2022
Publication title -
proceedings of the 15th international joint conference on biomedical engineering systems and technologies
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5220/0010976700003123
Subject(s) - distress , mental health , addiction , opioid , mental distress , emotional distress , narrative , social media , psychology , psychiatry , medicine , clinical psychology , computer science , world wide web , anxiety , linguistics , philosophy , receptor
Psychiatric and medical disorders, social and family environment, and legal distress are important determinants of distress that impact the effectiveness of the treatment in opioid treatment program (OTP). This information is not routinely captured in electronic health record, but may be found in clinical notes. This study aims to explore the feasibility and effectiveness of natural language processing (NLP) strategy for identifying legal, social, mental and medical determinates of distress along with emotional pain rooted in family environment from clinical narratives of patients with opioid addiction, and then using this information to find its impact on OTP outcomes. Analysis in this study showed that mental and legal distress significantly impact the result of the treatment in OTP.