Background: In clinical diagnostic interviews, mental health professionals (MHPs) implement a care practice that involves asking open questions (eg, “What do you want from your life?” “What have you tried before to bring change in your life?”) while listening empathetically to patients. During these interviews, MHPs attempted to build a trusting human-centered relationship while collecting data necessary for professional medical and psychiatric care. Often, because of the social stigma of mental health disorders, patient discomfort in discussing their presenting problem may add additional complexities and nuances to the language they use, that is, hidden signals among noisy content. Therefore, a focused, well-formed, and elaborative summary of clinical interviews is critical to MHPs in making informed decisions by enabling a more profound exploration of a patient’s behavior, especially when it endangers life. Objective: The aim of this study is to propose an unsupervised, knowledge-infused abstractive summarization (KiAS) approach that generates summaries to enable MHPs to perform a well-informed follow-up with patients to improve the existing summarization methods built on frequency heuristics by creating more informative summaries. Methods: Our approach incorporated domain knowledge from the Patient Health Questionnaire-9 lexicon into an integer linear programming framework that optimizes linguistic quality and informativeness. We used 3 baseline approaches: extractive summarization using the SumBasic algorithm, abstractive summarization using integer linear programming without the infusion of knowledge, and abstraction over extractive summarization to evaluate the performance of KiAS. The capability of KiAS on the Distress Analysis Interview Corpus-Wizard of Oz data set was demonstrated through interpretable qualitative and quantitative evaluations. Results: KiAS generates summaries (7 sentences on average) that capture informative questions and responses exchanged during long (58 sentences on average), ambiguous, and sparse clinical diagnostic interviews. The summaries generated using KiAS improved upon the 3 baselines by 23.3%, 4.4%, 2.5%, and 2.2% for thematic overlap, Flesch Reading Ease, contextual similarity, and Jensen Shannon divergence, respectively. On the Recall-Oriented Understudy for Gisting Evaluation-2 and Recall-Oriented Understudy for Gisting Evaluation-L metrics, KiAS showed an improvement of 61% and 49%, respectively. We validated the quality of the generated summaries through visual inspection and substantial interrater agreement from MHPs. Conclusions: Our collaborator MHPs observed the potential utility and significant impact of KiAS in leveraging valuable but voluminous communications that take place outside of normally scheduled clinical appointments. This study shows promise in generating semantically relevant summaries that will help MHPs make informed decisions about patient status.

中文翻译：

---

知识注入的临床诊断访谈摘要总结：框架开发研究

背景：在临床诊断面试中，精神卫生专业人员（MHP）实施的护理实践包括在聆听时询问一些开放性问题（例如，“您想从生活中获得什么？”“您曾尝试过什么以带来生活上的改变？”）。对病人有同情心。在这些访谈中，MHP试图建立一种以人为本的信任关系，同时收集专业医疗和精神病护理所需的数据。通常，由于精神健康障碍的社会污名，患者在讨论其出现问题时的不适感可能会给他们使用的语言增加额外的复杂性和细微差别，也就是说，在嘈杂的内容中隐藏了信号。因此，重点突出，格式正确，目的：本研究的目的是提出一种无监督，知识注入的抽象总结（KiAS）方法，该方法可生成摘要，以使MHP能够对患者进行充分知情的随访，以改善基于频率启发法的现有总结方法。创建更多信息摘要。方法：我们的方法将来自Patient Health Questionnaire-9词典的领域知识整合到一个整数线性编程框架中，该框架可优化语言质量和信息性。我们使用了3种基线方法：使用SumBasic算法进行提取摘要，使用不带知识注入的整数线性规划进行抽象摘要以及通过提取摘要进行抽象来评估KiAS的性能。通过可解释的定性和定量评估，证明了KiAS在Oz数据遇险分析访谈语料库-向导中的功能。结果：KiAS生成摘要（平均7个句子），以捕获信息性问题和长时间（平均58个句子），模棱两可和稀疏的临床诊断访谈中交换的信息。使用KiAS生成的摘要在3个基准上的主题重叠，Flesch Reading Ease，上下文相似性和Jensen Shannon差异分别改善了23.3％，4.4％，2.5％和2.2％。在针对回忆评估的回忆导向型2和针对回忆评估的L评价指标的上次评估中，KiAS分别提高了61％和49％。我们通过目视检查和MHP之间的实质性相互同意来验证所生成摘要的质量。结论：我们的合作者MHP观察到KiAS在利用正常计划的临床预约以外发生的宝贵而大量的交流中的潜在效用和重大影响。这项研究显示了产生语义上相关的摘要的希望，这些摘要将帮助MHP做出有关患者状况的明智决定。