利用语言模型进行精神状态检查的摘要：全面评估和数据集发布
Leveraging language models for summarizing mental state examinations: A comprehensive evaluation and dataset release
AI总结：
本研究利用语言模型对MSE进行摘要生成，通过开发问卷和收集数据，评估多种模型性能，最终发布数据集和模型供心理健康研究使用。
摘要：
精神健康障碍影响了全球相当大的人口，诊断主要通过精神状态检查（MSE）进行。MSE是一种结构化的评估，用于评估行为和认知功能在不同领域的表现，帮助心理健康专业人员进行诊断和治疗监测。然而，在发展中国家，心理健康支持的可及性有限，导致对心理健康专业人员的需求巨大。住院医生通常进行初步患者评估并为高级医生创建摘要，但他们的可用性受到限制，导致患者等待时间延长。 本研究通过评估各种语言模型来解决从MSE生成简洁摘要的挑战。鉴于相关心理健康对话数据集的匮乏，我们开发了一个12项描述性MSE问卷，并从405名参与者处收集了回应，产生了涵盖多种心理健康方面的9720条陈述。随后，我们评估了五个著名的预训练总结模型在总结MSE时的性能，包括有和无微调的情况。我们的全面评估，利用ROUGE、SummaC和人工评估等指标，证明语言模型能够为医生生成自动且连贯的MSE摘要。通过本文，我们发布了我们收集的对话数据集和训练好的模型，供心理健康研究社区使用。
英文摘要：
Mental health disorders affect a significant portion of the global population, with diagnoses primarily conducted through Mental State Examinations (MSEs). MSEs serve as structured assessments to evaluate behavioral and cognitive functioning across various domains, aiding mental health professionals in diagnosis and treatment monitoring. However, in developing countries, access to mental health support is limited, leading to an overwhelming demand for mental health professionals. Resident doctors often conduct initial patient assessments and create summaries for senior doctors, but their availability is constrained, resulting in extended patient wait times. This study addresses the challenge of generating concise summaries from MSEs through the evaluation of various language models. Given the scarcity of relevant mental health conversation datasets, we developed a 12-item descriptive MSE questionnaire and collected responses from 405 participants, resulting in 9720 utterances covering diverse mental health aspects. Subsequently, we assessed the performance of five well-known pre-trained summarization models, both with and without fine-tuning, for summarizing MSEs. Our comprehensive evaluation, leveraging metrics such as ROUGE, SummaC, and human evaluation, demonstrates that language models can generate automated coherent MSE summaries for doctors. With this paper, we release our collected conversational dataset and trained models publicly for the mental health research community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
