Machine learning for critical care: An overview and a sepsis case study

Abstract

Biology in general and medicine and healthcare in particular are facing the critical challenge of exponentially increasing data availability. The core of this challenge is putting these data to work through computer-based knowledge extraction methods. In the medical context this could take the form of medical decision support systems for diagnosis, prognosis or general management. Arguably, one of the most data dependent clinical environments is the critical care unit and by extension the whole area of critical care. Fresh approaches to data analysis in critical care are required, and Computational Intelligence and Machine Learning methods have already shown their usefulness in tackling problems in the area. This brief paper aims to be an introduction to the use of such methods in critical care.