Thesis Defense - Public Seminar: INTERPRETABLE MACHINE LEARNING SCORING SYSTEMS FOR EMERGENCY CARE

ABSTRACT:

The emergency department (ED) is usually the starting point of the patient flow through a hospital for urgent care, which is high-workload, time-sensitive, information-intensive, and life-critical. Risk stratification plays a part in the decision-making in prioritizing patients, treatment, and level of monitoring. For this purpose, an inherently interpretable model that allows doctors to easily understand how the model makes predictions is highly preferred. As a representative, scoring systems have been widely used in clinical settings. This thesis advanced the interpretable machine learning scoring system for emergency care. First, I established a ten-year large-scale Electronic Health Records (EHR) database of ED patients and identified critical factors of inpatient mortality. Second, I invented a new methodology, AutoScore, to automatically generate scoring systems using EHR based on interpretable machine learning. On this basis, a parsimonious and point-based scoring tool, the Score for Emergency Risk Prediction (SERP), was developed for triaging patients at the ED. Then, I extended AutoScore to survival data and derived the Score for Emergency Readmission Prediction (SERAP) for estimating time to emergency readmission. Finally, I explored deep learning in EHR with temporal patterns through a systematic review. Overall, this work provides evidence to support intervention by scoring systems to improve emergency care.

THESIS ADVISOR:  
A/Prof. Bibhas Chakraborty

ZOOM ID:
883 3483 8948

ZOOM PW:
415971