Quantifying the Association Between Psychotherapy Content and Clinical Outcomes Using Deep Learning
Quantifying the Association Between Psychotherapy Content and Clinical Outcomes Using Deep Learning
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DOI：
10.1001/jamapsychiatry.2019.2664
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发表时间：
2020-01-01
期刊：
影响因子：
25.8
通讯作者：
Blackwell, Andrew D.
中科院分区：
文献类型：
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作者：
Ewbank, Michael P.;Cummins, Ronan;Blackwell, Andrew D.
与身体状况的治疗相比，精神健康障碍的护理质量仍然很差，治疗的改善速度缓慢，主要原因是缺乏客观和系统的方法来衡量心理治疗的效果。目的：将深度学习模型应用于认知行为治疗（CBT）会话记录的大规模临床数据集，以产生可量化的治疗量，并确定治疗各方面的量与临床结果之间的关系。设计、环境和参与者所有数据均来自英国2012年6月至2018年3月期间接受互联网支持的CBT治疗精神健康障碍的患者。认知行为治疗通过即时同步消息在一个安全的在线治疗室进行。初始样本共包括17 572名患者（90 934个治疗过程记录）。患者自行转诊或由初级卫生保健工作者直接转诊到该服务机构。所有患者均接受由合格的CBT治疗师提供的国家健康与护理卓越研究所批准的疾病特异性CBT治疗方案。主要结果和测量临床结果是根据患者症状和治疗参与的可靠改善来衡量的。可靠的改善是根据两项严重程度测量来计算的：患者健康问卷（PHQ-9）和广泛性焦虑障碍7项量表（GAD-7），分别对应于抑郁和焦虑症状，由患者在初始评估和每次治疗前完成。结果：最终分析纳入了14 899例患者（10 882例女性）的治疗疗程，年龄在18至94岁之间（中位年龄34.8岁）。我们训练了一个深度学习模型，将治疗师的话语自动分类为24个特征类别中的1个或多个。将训练好的模型应用于我们的数据集，以获得所提供治疗的每个特征的可量化度量。逻辑回归显示，包括改变方法（CBT中使用的认知和行为技术）在内的一些治疗特征的数量增加，与患者症状可靠改善的可能性增加（优势比，1.11;95% CI, 1.06-1.17）和患者参与（优势比，1.20,95% CI, 1.12-1.27）相关。非治疗相关内容的数量与症状改善的几率降低（优势比，0.89;95% CI, 0.85-0.92）和患者参与（优势比，0.88,95% CI, 0.84-0.92）相关。结论和相关性这项工作证明了心理治疗的临床结果与治疗师话语内容之间的关联。这些发现支持了CBT改变方法有助于改善患者表现症状的原则。将深度学习应用于大型临床数据集可以为心理治疗提供有价值的见解，为新疗法的开发提供信息，并有助于规范临床实践。
IMPORTANCE Compared with the treatment of physical conditions, the quality of care of mental health disorders remains poor and the rate of improvement in treatment is slow, a primary reason being the lack of objective and systematic methods for measuring the delivery of psychotherapy.OBJECTIVE To use a deep learning model applied to a large-scale clinical data set of cognitive behavioral therapy (CBT) session transcripts to generate a quantifiable measure of treatment delivered and to determine the association between the quantity of each aspect of therapy delivered and clinical outcomes.DESIGN, SETTING, AND PARTICIPANTS All data were obtained from patients receiving internet-enabled CBT for the treatment of a mental health disorder between June 2012 and March 2018 in England. Cognitive behavioral therapy was delivered in a secure online therapy room via instant synchronous messaging. The initial sample comprised a total of 17 572 patients (90 934 therapy session transcripts). Patients self-referred or were referred by a primary health care worker directly to the service.EXPOSURES All patients received National Institute for Heath and Care Excellence-approved disorder-specific CBT treatment protocols delivered by a qualified CBT therapist.MAIN OUTCOMES AND MEASURES Clinical outcomes were measured in terms of reliable improvement in patient symptoms and treatment engagement. Reliable improvement was calculated based on 2 severity measures: Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7), corresponding to depressive and anxiety symptoms respectively, completed by the patient at initial assessment and before every therapy session.RESULTS Treatment sessions from a total of 14 899 patients (10 882 women) aged between 18 and 94 years (median age, 34.8 years) were included in the final analysis. We trained a deep learning model to automatically categorize therapist utterances into 1 or more of 24 feature categories. The trained model was applied to our data set to obtain quantifiable measures of each feature of treatment delivered. A logistic regression revealed that increased quantities of a number of session features, including change methods (cognitive and behavioral techniques used in CBT), were associated with greater odds of reliable improvement in patient symptoms (odds ratio, 1.11; 95% CI, 1.06-1.17) and patient engagement (odds ratio, 1.20, 95% CI, 1.12-1.27). The quantity of nontherapy-related content was associated with reduced odds of symptom improvement (odds ratio, 0.89; 95% CI, 0.85-0.92) and patient engagement (odds ratio, 0.88, 95% CI, 0.84-0.92).CONCLUSIONS AND RELEVANCE This work demonstrates an association between clinical outcomes in psychotherapy and the content of therapist utterances. These findings support the principle that CBT change methods help produce improvements in patients' presenting symptoms. The application of deep learning to large clinical data sets can provide valuable insights into psychotherapy, informing the development of new treatments and helping standardize clinical practice.