Abstract
BackgroundDiabetes 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.MethodsWe 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.ResultsThe 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.ConclusionUMLS 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.
Highlights
Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia
We measured the degree of organ dysfunction using the sequential organ failure assessment (SOFA) score [23] in patients admitted to the intensive care unit (ICU)
In this study, we developed several predictive models to interpret the mortality of diabetes mellitus patients admitted in ICU
Summary
Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The rate of incidence and prevalence of patients with Diabetes mellitus type 2 among adults is increasing over time and has led to an increase in the number of patients admitted in the intensive care unit (ICU). These diabetic patients use more than 45% of resources in the ICU compared to the patients associated with other chronic diseases [1]. It is well known that patients admitted in ICU due to diabetes are more prone to diseases and risk complication; one of these risk factors is due to the hampered immune cell response to the disease [2]. A few studies have been conducted on the mortality of diabetes mellitus patients; most of them are limited to factors associated with the increased mortality in the ICU setting [3]
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