Abstract
A major roadblock in the seamless digitization of medical records remains the lack of interoperability of existing records. Extracting relevant medical information required for further treatment planning or even research is a time consuming labour intensive task involving the much valuable time of doctors. In this demo paper we present, MedPromptExtract an automated tool using a combination of semi supervised learning, large language models, natural lanuguage processing and prompt engineering to convert unstructured medical records to structured data which is amenable to further analysis.
Abstract (translated)
在医疗记录的无缝数字化过程中，一个主要障碍是现有记录之间的不兼容性。从进一步治疗计划或研究提取相关医疗信息是一个耗时且劳动密集型任务，涉及医生们宝贵的时间。在本文演示论文中，我们提出了MedPromptExtract，一种使用半监督学习、大型语言模型、自然语言处理和提示工程相结合的自动工具，将无结构医疗记录转换为可以进一步分析的结构化数据。
URL
https://arxiv.org/abs/2405.02664
