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使用自然语言处理对阿片类药物治疗项目中患者痛苦的比较分析

Comparative Analysis of Patient Distress in Opioid Treatment Programs using Natural Language Processing.

作者信息

Shah-Mohammadi Fatemeh, Cui Wanting, Bachi Keren, Hurd Yasmin, Finkelstein Joseph

机构信息

Icahn School of Medicine at Mount Sinai, New York, NY, U.S.A.

出版信息

Biomed Eng Syst Technol Int Jt Conf BIOSTEC Revis Sel Pap. 2022 Feb;2022:319-326. doi: 10.5220/0010976700003123.

DOI:10.5220/0010976700003123
PMID:35265945
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8903064/
Abstract

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.

摘要

精神疾病和医学疾病、社会及家庭环境以及法律困扰是痛苦的重要决定因素，会影响阿片类药物治疗项目（OTP）的治疗效果。这些信息在电子健康记录中并非常规记录，但可能在临床笔记中找到。本研究旨在探索自然语言处理（NLP）策略的可行性和有效性，该策略用于从阿片类药物成瘾患者的临床叙述中识别痛苦的法律、社会、心理和医学决定因素以及源于家庭环境的情感痛苦，然后利用这些信息来发现其对OTP结果的影响。本研究分析表明，心理和法律困扰对OTP的治疗结果有显著影响。