Applying AI to manage acute and chronic clinical conditions

Charles Gillan, Rachael Hagan*, Murali Shyamsundar

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

Computer systems deployed in hospital environments, particularly physiological and biochemical real time monitoring of patients in an Intensive Care Unit (ICU) environment, routinely collect a large volume of data that can hold very useful information. However, the vast majority are either not stored and lost forever or is stored in digital archives and seldom re-examined. In recent years, there has been extensive work carried out by researchers utilizing Machine Learning (ML) and Artificial Intelligence (AI) techniques on these data streams, to predict and pre-vent disease states. Such work aims to improve patient outcomes, to decrease mortality rates and decrease hospital stays and more generally, to decrease healthcare costs.

This chapter reviews the state-of-the-art in that field and reports on our own cur-rent research, with practicing clinicians, on improving ventilator weaning protocols and lung protective ventilation, using ML and AI methodologies for decision sup-port, including but not limited to Neural Networks and Decision Trees. The chapter considers both the clinical and Computer Science aspects of the field. In addition, we look to the future and report how physiological data holds clinically important information to aid in decision support in the wider hospital environment.
Original languageEnglish
Title of host publicationTechnologies and Applications for Big Data Value
PublisherSpringer
Publication statusAccepted - 25 Jan 2021

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