@文章{信息:doi/10.2196/38168，作者=“Malins, Sam和Figueredo, Grazziela和Jilani, Tahseen和Long, yunseen和Andrews, Jacob和Rawsthorne, Mat和Manolescu, Cosmin和Clos, Jeremie和Higton, Fred和Waldram, David和Hunt, Daniel和Perez Vallejos, Elvira和Moghaddam, Nima”，标题=“开发心理治疗中患者激活的自动评估:，期刊="JMIR Med Inform"，年="2022"，月="11月"，日="8"，卷="10"，数="11"，页数="e38168"，关键词="负责任的人工智能;机器学习;认知行为疗法;multimorbidity;自然语言处理;背景:患者激活被定义为患者管理自己健康的信心和感知能力。患者激活一直是长期健康和护理成本的一致预测指标，特别是对于患有多种长期健康疾病的人。然而，目前还没有办法衡量患者在医疗咨询中所说的话的激活程度。这对心理治疗来说可能尤其重要，因为目前大多数评估治疗内容的方法由于时间和成本的限制不能常规使用。 Natural language processing (NLP) has been used increasingly to classify and evaluate the contents of psychological therapy. This aims to make the routine, systematic evaluation of psychological therapy contents more accessible in terms of time and cost restraints. However, comparatively little attention has been paid to algorithmic trust and interpretability, with few studies in the field involving end users or stakeholders in algorithm development. Objective: This study applied a responsible design to use NLP in the development of an artificial intelligence model to automate the ratings assigned by a psychological therapy process measure: the consultation interactions coding scheme (CICS). The CICS assesses the level of patient activation observable from turn-by-turn psychological therapy interactions. Methods: With consent, 128 sessions of remotely delivered cognitive behavioral therapy from 53 participants experiencing multiple physical and mental health problems were anonymously transcribed and rated by trained human CICS coders. Using participatory methodology, a multidisciplinary team proposed candidate language features that they thought would discriminate between high and low patient activation. The team included service-user researchers, psychological therapists, applied linguists, digital research experts, artificial intelligence ethics researchers, and NLP researchers. Identified language features were extracted from the transcripts alongside demographic features, and machine learning was applied using k-nearest neighbors and bagged trees algorithms to assess whether in-session patient activation and interaction types could be accurately classified. Results: The k-nearest neighbors classifier obtained 73{\%} accuracy (82{\%} precision and 80{\%} recall) in a test data set. The bagged trees classifier obtained 81{\%} accuracy for test data (87{\%} precision and 75{\%} recall) in differentiating between interactions rated high in patient activation and those rated low or neutral. Conclusions: Coproduced language features identified through a multidisciplinary collaboration can be used to discriminate among psychological therapy session contents based on patient activation among patients experiencing multiple long-term physical and mental health conditions. ", issn="2291-9694", doi="10.2196/38168", url="https://medinform.www.mybigtv.com/2022/11/e38168", url="https://doi.org/10.2196/38168", url="http://www.ncbi.nlm.nih.gov/pubmed/36346654" }