Koonce Taneya Y, Giuse Dario A, Williams Annette M, Blasingame Mallory N, Krump Poppy A, Su Jing, Giuse Nunzia B
Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States.
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.
JMIR Med Inform. 2024 Jan 30;12:e53516. doi: 10.2196/53516.
Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC's electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies.
应用人工智能从大规模真实世界临床数据集中提取见解,可以补充和加强健康科学研究与临床护理的知识管理工作。在范德堡大学医学中心(VUMC),内部开发的词云自然语言处理系统使用统一医学语言系统术语,从VUMC电子健康记录存储库中的患者记录中提取编码概念。通过这个过程,词云提取特定患者或人群临床文档中最突出的概念。词云为临床护理决策和研究提供了附加价值。这篇观点论文描述了一个用例,即VUMC知识管理中心如何利用词云所代表的病症-疾病关联,来辅助生成解读全表型关联研究所需的知识。