Hybrid Mortality Prediction using Multiple Source Systems

TL;DR

This paper presents a hybrid AI-based model that integrates multiple clinical systems to improve mortality prediction accuracy for hospitalized diabetic patients, outperforming traditional models.

Contribution

It introduces a novel machine learning approach that combines data from ICU, diabetes, and comorbidities to enhance mortality prediction in clinical settings.

Findings

Improved mortality prediction accuracy over non-AI models

Effective feature selection from clinical data

Demonstrated benefits of integrating multiple systems

Abstract

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).