Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the best allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study aimed to exploit machine learning technologies to cope with this challenge. The study was based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the conventional decision tree (DT) models built with only 6 features, including age, gender, and four comorbidities, delivered the same level of prediction accuracy as the state-of-the-art deep neural network models built with 18 features. Accordingly, we further studied how to exploit the DT models with different sensitivity levels to determine patient triage and optimize medical resource allocation in different stages of an EID disaster to aid the frontline clinicians and policy-makers. In conclusion, our study demonstrated an approach to exploit machine learning technologies to cope with the challenges during the outbreak of an EID.
Supervised Machine Learning for Risk Stratification of Influenza-Like Illness: A Model to Prioritize Emerging Infectious Disease Disaster Responses