Title:  Predicting in-hospital mortality for ICU patients with liver disease using process mining and deep learning
Speakers: Zonglin Ji
Abstract:

Intensive Care Unit (ICU) patients with liver disease present a unique clinical challenge due to increased risks of complications and mortality. Traditional severity scoring methods, while helpful, do not comprehensively consider a patient’s prior medical history nor the temporal events associated with their stay, and are thus limited when it comes to providing an up-to-date understanding of a patient’s condition as it evolves. This research aims to incorporate time series events from the care pathways of patients to improve on this situation and enable better survival predictions over time. In this talk, we describe our work on combining Process Mining and Deep Learning and applying it to mortality predictions for ICU patients with liver diseases.