Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Tencent, Shenzhen, China.
BMC Med Inform Decis Mak. 2020 Dec 30;20(Suppl 11):295. doi: 10.1186/s12911-020-01318-4.
Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality.
We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations.
The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients.
UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.
糖尿病是一种常见的代谢性疾病，其特征为慢性高血糖。医疗保健数据的激增正在加速精准医疗和个性化医疗的发展。人工智能和基于算法的方法对于支持临床决策变得越来越重要。这些方法能够通过减轻医疗保健提供者的一些日常工作并使他们能够专注于关键问题，来增强医疗保健提供者的能力。然而，很少有研究使用预测模型来发现 ICU 患者合并症与糖尿病之间的关联。本研究旨在使用统一医学语言系统（UMLS）资源，涉及机器学习和自然语言处理（NLP）方法来预测死亡率的风险。
我们对医疗信息监护 III （MIMIC-III）数据进行了二次分析。应用了不同的机器学习建模和 NLP 方法。医疗保健领域的领域知识建立在由专家创建的字典之上，这些字典定义了药物或临床症状等临床术语。这种知识对于从文本注释中识别断言某种疾病的信息非常有价值。知识引导模型可以自动从包含这些各种概念之间的概念实体和关系的临床笔记或生物医学文献中提取知识。死亡率分类是基于知识引导特征和规则的组合进行的。应用了 UMLS 实体嵌入和带有单词嵌入的卷积神经网络（CNN）。使用具有实体嵌入的概念唯一标识符（CUI）来构建临床文本表示。
所采用的机器学习模型的最佳配置产生了有竞争力的 AUC 为 0.97。机器学习模型与临床笔记的 NLP 有望帮助医疗保健提供者预测危重症患者的死亡率风险。
UMLS 资源和临床笔记是预测重症监护环境中糖尿病患者死亡率的强大而重要的工具。知识引导的 CNN 模型对于学习隐藏特征是有效的（AUC=0.97）。
