Machine Learning Methods in Intensive Care Units for Clinical Decision Support

PhD Thesis Proposal Defence


Title: "Machine Learning Methods in Intensive Care Units for Clinical Decision 
Support"

by

Miss Hyunjung KWAK


Abstract:

Machine learning has become a trend in recent years and has opened up a
whole new era of research in various fields. The potential benefits of
machine learning in healthcare have been demonstrated, and the demand for
machine learning has also increased. An intensive care unit (ICU) is a
special department in a hospital for critical care medicine for patients
in serious condition, and research on ICU data can help patients and
medical practitioners from risk predictions to treatment planning.

In this thesis, we study machine learning approaches that extract high-value 
patterns from electronic health records in ICUs with clinical expertise and 
support physicians and patients based on real-world problems. We provide 
medical predictions and recommendations, the most common research goals, based 
on common diseases such as AKI, sepsis, and heart disease as well as intubation 
and medications applied to many patients. In the meanwhile, we take into 
account multiple challenges in data and algorithms on the practical side, which 
are important in clinical studies, but often neglected during the algorithm 
development stage.

First, we propose a scoring system to predict the need for intubation in 24 
hours at the ICU admission, demonstrating the scalability with only easily 
collectible bedside parameters. Second, we verify bias control with data 
matching, and study vasopressor required (shock) events using clinical vital 
signs generally accessible for all patients within 24 hours of admission to the 
ICU. Third, we deviate from the knowledge or prejudice that hyperkalemia is a 
complication of AKI and study the prediction of hyperkalemia through multiple 
clinical scenarios and lead times. Fourth, we demonstrate how to build a 
structured database from clinical notes for heart disease in the ICU and 
utilize it in conjunction with other data from electronic health records to 
improve the prediction of medical outcomes.


Date:			Friday, 30 April 2021

Time:                  	10:00am - 12:00noon

Zoom Meeting:		https://hkust.zoom.us/j/4341775548

Committee Members:	Dr. Pan Hui (Supervisor)
 			Prof. Raymond Wong (Chairperson)
 			Prof. James Kwok
 			Dr. Hao Chen


**** ALL are Welcome ****