Title: Understanding the Clinical Pathways of Intensive Care Patients using AI
Speaker: Zonglin Ji
Abstract:

Clinical pathway analysis is pivotal in ensuring specialised, standardised, normalised and sophisticated therapy procedures. Understanding the patterns behind clinical pathways, especially what factors may cause them to deviate from expected standard procedures, are critical to providing decision support for clinicians and increasing the efficiency of medical care. Although existing pattern mining techniques can tell us about the sequence of medical behaviours in clinical pathways, few studies directly relate pathway patterns to the physiological data of patients. In this PhD project, we will explore how patient health data can affect and inform clinical pathways, especially at the early stage of hospital admission. We aim to predict the potential pathway that a new intensive care patient may take, focusing on possible deviations as they may indicate complications in their conditions and associated treatments. The ultimate goal of this project is to develop a novel framework that can dynamically predict patient pathways over time, thus providing continuous decision support for clinicians over ICU patients.