TY - JOUR AU - Malins, Sam AU - Figueredo, Grazziela AU - Jilani, Tahseen AU - Long, Yunfei AU - Andrews, Jacob AU - Rawsthorne, Mat AU - Manolescu, Cosmin AU - Clos, Jeremie AU - Higton, Fred AU - Waldram, David AU - Hunt, Daniel AU - Perez Vallejos, Elvira AU - Moghaddam, Nima PY - 2022 DA - 2022/11/8 TI -开发心理治疗中患者激活的自动评估:联合开发方法JO - JMIR Med Inform SP - e38168 VL - 10 IS - 11 KW -负责人工智能KW -机器学习KW -认知行为治疗KW -多病KW -自然语言处理KW -心理健康AB -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。自然语言处理(NLP)越来越多地被用于心理治疗内容的分类和评价。旨在使心理治疗内容的常规、系统评估在时间和成本方面更容易获得。然而，对算法信任和可解释性的关注相对较少，涉及最终用户或利益相关者的算法开发领域的研究很少。目的:本研究采用负责任的设计，将NLP应用于人工智能模型的开发，以自动化心理治疗过程测量:咨询交互编码方案(CICS)所分配的评分。CICS评估从心理治疗相互作用中观察到的患者激活水平。 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. SN - 2291-9694 UR - https://medinform.www.mybigtv.com/2022/11/e38168 UR - https://doi.org/10.2196/38168 UR - http://www.ncbi.nlm.nih.gov/pubmed/36346654 DO - 10.2196/38168 ID - info:doi/10.2196/38168 ER -