Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.
Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.
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DOI：
10.1186/s12911-020-01318-4
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发表时间：
2020-12-30
影响因子：
3.5
通讯作者：
Luo Y
中科院分区：
文献类型：
--
作者：
Ye J;Yao L;Shen J;Janarthanam R;Luo Y
糖尿病是一种以慢性高血糖为特征的普遍代谢性疾病。医疗数据的雪崩正在加速医疗的精准化和个性化。人工智能和基于算法的方法在支持临床决策方面变得越来越重要。这些方法减轻了卫生保健提供者的一些日常工作，使他们能够专注于关键问题，从而增强了他们的能力。然而，很少有研究使用预测模型来揭示ICU患者合并症与糖尿病之间的关系。本研究旨在使用统一医学语言系统（UMLS）资源，包括机器学习和自然语言处理（NLP）方法来预测死亡风险。我们对重症监护医学信息集市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。卫生保健领域的知识是建立在定义临床术语（如药物或临床症状）的专家创建的词典上的。这一知识对于从断言某种疾病的文本注释中识别信息是有价值的。知识引导模型可以自动从包含概念实体和这些概念之间关系的临床记录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与规则相结合的方法。将UMLS实体嵌入和卷积神经网络（CNN）与词嵌入相结合。使用概念唯一标识符（gui）和实体嵌入来构建临床文本表示。所使用的机器学习模型的最佳配置产生了0.97的具有竞争力的AUC。机器学习模型和临床笔记的NLP有望帮助医疗保健提供者预测危重患者的死亡风险。UMLS资源和临床记录是预测重症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）。
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.
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影响因子：
3.5
作者：
Weng WH;Wagholikar KB;McCray AT;Szolovits P;Chueh HC
通讯作者：
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作者：
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