Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4415-4420. doi: 10.1109/EMBC48229.2022.9871960.
Potential of natural language processing (NLP) in extracting patient's information from clinical notes of opioid treatment programs (OTP) and leveraging it in development of predictive models has not been fully explored. The goal of this study was to assess potential of NLP in identifying legal, social, mental, medical and family environment-based determinants of distress from clinical narratives of patients with opioid addiction, and then using this information in predicting OTP outcomes. Around 63% of patients reported improvements after completing OTP. We compared the results of logistics regression and random forest for predictive modeling. Random forest model performed slightly better than logistic regression (75% F1 score) with 74% accuracy. Clinical Relevance- Psychiatric and medical disorders, social, legal and family-based distress are important determinants of distress in patients enrolled in OTP. These information are often recorded in clinical notes. Extraction of this information and their utilization as features in machine learning models will lead to the enhancement of the performance of the OTP outcome predictive models.
自然语言处理（NLP）在从阿片类药物治疗计划（OTP）的临床记录中提取患者信息并利用其开发预测模型方面的潜力尚未得到充分探索。本研究的目的是评估 NLP 从阿片类药物成瘾患者的临床叙述中识别法律、社会、心理、医疗和家庭环境相关困扰决定因素的潜力，然后利用这些信息预测 OTP 结果。大约 63%的患者在完成 OTP 后报告有所改善。我们比较了逻辑回归和随机森林在预测建模方面的结果。随机森林模型的表现略优于逻辑回归（75%的 F1 得分），准确率为 74%。临床相关性-精神和医疗障碍、社会、法律和家庭困扰是 OTP 入组患者困扰的重要决定因素。这些信息通常记录在临床记录中。提取这些信息并将其用作机器学习模型中的特征，将提高 OTP 结果预测模型的性能。
