Role of AI in Mortality Prediction in Intensive Care Unit Patients

Abstract

Mortality rate and risk prediction in the intensive care unit (ICU) are two major concerns in hospitals. Prediction is highly dependent on the correct analysis of available data. Many data analysis techniques can provide sufficient information to correctly predict the survival of ICU patients during their admission time in the hospital. Despite methodological advances in machine learning algorithms, clinicians often use black box model. This flaw creates a difficulty in translating complex clinical parameters to corresponding model features. Regarding mortality prediction, it is found that different types of machine learning algorithms have different features that can be properly selected for the respective problem. This chapter focuses on the uses of the types of machine learning algorithms relevant to correct prediction related to survival of the patients.

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