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Journal Article
Machine learning for emerging infectious disease field responses
2022
Overview
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 optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been 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 decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 692/499
/ Communicable Diseases, Emerging - epidemiology
/ Communicable Diseases, Emerging - prevention & control
/ COVID-19
/ COVID-19 - prevention & control
/ Hospitalization - statistics & numerical data
/ Humanities and Social Sciences
/ Humans
/ International Classification of Diseases
/ Pandemics - prevention & control
/ Patients
/ Preventive Medicine - methods
/ Preventive Medicine - statistics & numerical data
/ Public Health - statistics & numerical data
/ Science
