Manas Gaur, Aribandi Vamsi, Kursuncu Ugur, Alambo Amanuel, Shalin Valerie L, Thirunarayan Krishnaprasad, Beich Jonathan, Narasimhan Meera, Sheth Amit
Artificial Intelligence Institute, University of South Carolina, Columbia, SC, United States.
Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, OH, United States.
JMIR Ment Health. 2021 May 10;8(5):e20865. doi: 10.2196/20865.
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.
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.
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.
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.
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.
在临床诊断访谈中，心理健康专业人员（MHP）实施一种护理实践，包括提出开放性问题（例如，“你希望从生活中得到什么？”“你之前尝试过什么来改变你的生活？”），同时以同理心倾听患者。在这些访谈中，MHP试图建立一种信任的、以患者为中心的关系，同时收集专业医疗和精神护理所需的数据。通常，由于心理健康障碍的社会污名，患者在讨论其当前问题时的不适可能会给他们使用的语言增加额外的复杂性和细微差别，即在嘈杂内容中的隐藏信号。因此，对临床访谈进行重点突出、结构良好且详尽的总结对于MHP做出明智决策至关重要，这能使他们更深入地探究患者的行为，尤其是当行为危及生命时。
本研究的目的是提出一种无监督的、知识注入的抽象摘要（KiAS）方法，该方法生成的摘要能使MHP对患者进行明智的后续跟进，通过创建更具信息性的摘要来改进基于频率启发式的现有摘要方法。
我们的方法将来自患者健康问卷-9词汇表的领域知识纳入一个整数线性规划框架，该框架优化语言质量和信息性。我们使用了3种基线方法：使用SumBasic算法的抽取式摘要、不注入知识的整数线性规划抽象式摘要以及抽取式摘要之上的抽象，以评估KiAS的性能。通过可解释的定性和定量评估，展示了KiAS在痛苦分析访谈语料库-绿野仙踪数据集中的能力。
KiAS生成的摘要（平均7个句子）捕捉了在长（平均58个句子）、模糊且稀疏的临床诊断访谈中交换的信息性问题和回答。使用KiAS生成的摘要在主题重叠、弗莱什易读性、上下文相似度和詹森-香农散度方面分别比3种基线方法提高了23.3%、4.4%、2.5%和2.2%。在面向召回的梗概评估-2和面向召回的梗概评估-L指标上，KiAS分别提高了61%和49%。我们通过目视检查和MHP之间高度的评分者间一致性验证了生成摘要的质量。
我们的合作MHP观察到KiAS在利用正常安排的临床预约之外发生的有价值但大量的沟通方面的潜在效用和重大影响。这项研究显示出有希望生成语义相关的摘要，这将有助于MHP对患者状况做出明智决策。
