Isaac Mativo1, Yelena Yesha1, Michael Grasso2, Tim Oates1, Qian Zhu3 1Department of Computer Science, University of Maryland Baltimore County (UMBC), 2School of Medicine, University of Maryland Baltimore (UMB), 3Department of Information Systems, University of Maryland Baltimore County (UMBC)
The use of artificial intelligence in clinical care to improve decision support systems is increasing. This is not surprising since by its very nature, the practice of medicine consists of making decisions based on observations from different systems both inside and outside the human body. In this paper, we combine three general systems (ICU, diabetes, and comorbidities) and use them to make patient clinical predictions. We use an artificial intelligence approach to show that we can improve mortality prediction of hospitalized diabetic patients. We do this by utilizing a machine learning approach to select clinical input features that are more likely to predict mortality. We then use these features to create a hybrid mortality prediction model and compare our results to non artificial intelligence models. For simplicity, we limit our input features to patient comorbidities and features derived from a well-known mortality measure, the Sequential Organ Failure Assessment (SOFA).
Decision Support Systems; Artificial Intelligence; Hybrid Systems.
