Hybrid Mortality Prediction using Multiple Source Systems
This work addresses mortality prediction for diabetic patients in clinical settings, but it is incremental as it applies existing methods to a specific domain.
The paper tackled mortality prediction for hospitalized diabetic patients by combining ICU, diabetes, and comorbidities data, resulting in improved predictions compared to non-AI models.
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).