Machine learning for emerging infectious disease field responses.

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.

National retrospective cohort study to identify age-specific fatality risks of comorbidities among hospitalised patients with influenza-like illness in Taiwan.

OBJECTIVES: This study aimed to examine comprehensively the prognostic impact of underlying comorbidities among hospitalised patients with influenza-like illness (ILI) in different age groups and provide recommendations targeting the vulnerable patients. SETTING AND PARTICIPANTS: A retrospective cohort of 83 227 hospitalised cases with ILI were identified from Taiwan's National Health Insurance Research Database from January 2005 to December 2010. Cases were stratified into three different age groups: paediatric (0-17 years), adult (18-64 years) and elderly (≧65 years), and their age, sex, comorbidity and past healthcare utilisation were analysed for ILI-associated fatality. MAIN OUTCOME MEASURES: ORs for ILI-related fatality in different age groups were performed using multivariable analyses with generalised estimating equation models and adjusted by age, sex and underlying comorbidities. RESULTS: Hospitalised ILI-related fatality significantly increased with comorbidities of cancer with metastasis (adjusted OR (aOR)=3.49, 95% CI: 3.16 to 3.86), haematological malignancy (aOR=3.02, 95% CI: 2.43 to 3.74), cancer without metastasis (aOR=1.72, 95% CI: 1.54 to 1.91), cerebrovascular (aOR=1.24, 95% CI: 1.15 to 1.33) and heart diseases (aOR=1.19, 95% CI: 1.11 to 1.27) for all age groups. Adult patients with AIDS; adult and elderly patients with chronic kidney disease, tuberculosis and diabetes were significantly associated with elevated risk of death. Severe liver diseases and hypothyroidism among elderly, and dementia/epilepsy among elderly and paediatrics were distinctively associated with likelihood of ILI-related fatality. CONCLUSIONS: Different age-specific comorbidities were associated with increasing risk of death among hospitalised ILI patients. These findings may help update guidelines for influenza vaccination and other prevention strategies in high-risk groups for minimising worldwide ILI-related deaths.