The discharge summary is a one of critical documents in the patient journey, encompassing all events experienced during hospitalization, including multiple visits, medications, tests, surgery/procedures, and admissions/discharge. Providing a summary of the patient's progress is crucial, as it significantly influences future care and planning. Consequently, clinicians face the laborious and resource-intensive task of manually collecting, organizing, and combining all the necessary data for a discharge summary. Therefore, we propose "NOTE", which stands for "Notable generation Of patient Text summaries through an Efficient approach based on direct preference optimization". NOTE is based on Medical Information Mart for Intensive Care- III dataset and summarizes a single hospitalization of a patient. Patient events are sequentially combined and used to generate a discharge summary for each hospitalization. In the present circumstances, large language models' application programming interfaces (LLMs' APIs) are widely available, but importing and exporting medical data presents significant challenges due to privacy protection policies in healthcare institutions. Moreover, to ensure optimal performance, it is essential to implement a lightweight model for internal server or program within the hospital. Therefore, we utilized DPO and parameter efficient fine tuning (PEFT) techniques to apply a fine-tuning method that guarantees superior performance. To demonstrate the practical application of the developed NOTE, we provide a webpage-based demonstration software. In the future, we will aim to deploy the software available for actual use by clinicians in hospital. NOTE can be utilized to generate various summaries not only discharge summaries but also throughout a patient's journey, thereby alleviating the labor-intensive workload of clinicians and aiming for increased efficiency.

翻译：摘要：出院总结是患者医疗过程中关键文件之一，涵盖住院期间经历的所有事件，包括多次就诊、用药、检查、手术/操作以及入院/出院。提供患者病情进展的总结至关重要，因为它显著影响后续治疗与规划。因此，临床医生需要手动收集、整理和组合出院总结所需的所有数据，这是一项费时费力的资源密集型任务。为此，我们提出"NOTE"模型，其全称为"基于直接偏好优化高效方法的患者文本摘要显著生成"。NOTE基于重症监护医学信息集市III数据集，总结患者单次住院经历。患者事件按序组合，用于生成每次住院的出院总结。在当前环境下，大语言模型应用程序接口（LLMs的API）已广泛可用，但因医疗机构的隐私保护政策，医学数据的导入导出面临显著挑战。此外，为确保最佳性能，需在医院内部服务器或程序中部署轻量级模型。因此，我们采用直接偏好优化和参数高效微调技术，应用了能够保证卓越性能的微调方法。为展示所开发NOTE的实际应用，我们提供了基于网页的演示软件。未来，我们将致力于部署该软件，使其可供临床医生在实际工作中使用。NOTE不仅能生成出院总结，还可用于患者全过程中各类总结的生成，从而减轻临床医生的劳动强度，提高工作效率。