Medical University of Vienna, Center for Medical Data Science, Institute of Artificial Intelligence, Spitalgasse 23, 1090 Vienna, Austria.
Medexter Healthcare, Borschkegasse 7/5, 1090 Vienna, Austria.
Stud Health Technol Inform. 2024 Aug 22;316:1822-1826. doi: 10.3233/SHTI240785.
We analyze five approaches to knowledge management in clinical decision support (CDS) systems: pattern recognition based on annotated imaging data, mining of stored structured medical data, text mining of published texts, computable knowledge design, and general or specific text corpora for large language models. Each method's strengths and limitations in automating clinical knowledge management while striving for a zero-error policy are evaluated, offering insights into their roles in enhancing healthcare through intelligent decision support. The study aims to inform decisions in the development of effective, transparent CDS systems in clinical and patient care settings.
我们分析了临床决策支持(CDS)系统中五种知识管理方法:基于标注影像数据的模式识别、存储结构化医疗数据的挖掘、已发表文本的文本挖掘、可计算知识设计,以及用于大型语言模型的通用或特定文本语料库。评估了每种方法在自动化临床知识管理的同时努力实现零错误策略方面的优缺点,探讨了它们在通过智能决策支持提高医疗保健水平方面的作用。本研究旨在为临床和患者护理环境中开发有效、透明的 CDS 系统的决策提供信息。