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Prediction Models for COVID-19 Mortality Using Artificial Intelligence.

Dong-Kyu Kim^1,2

¹Institute of New Frontier Research, Division of Big Data and Artificial Intelligence, Hallym University College of Medicine, Chuncheon 24253, Korea.

Journal of Personalized Medicine

|September 23, 2022

Summary

This summary is machine-generated.

Clinical prediction models for coronavirus disease 2019 (COVID-19) can aid healthcare systems. Machine learning models offer promising tools for predicting COVID-19 infection risk and patient outcomes.

Keywords:

COVID-19 artificial intelligence machine learning mortality prediction models systematic review

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Area of Science:

Medical Informatics
Public Health
Epidemiology

Background:

The COVID-19 pandemic significantly strained global healthcare systems, necessitating advanced tools for patient management.
Clinical decision-making for COVID-19 often relies on anecdotal evidence in the absence of robust predictive models.
Accurate prediction models are crucial for risk stratification and resource allocation during health crises.

Discussion:

Machine learning (ML) methods are increasingly explored for developing COVID-19 clinical prediction models.
Systematic reviews are vital for critically appraising the performance and applicability of these ML-based models.
The integration of ML into clinical practice can enhance the management of COVID-19 patients.

Key Insights:

ML-driven prediction models can estimate infection risk and forecast patient outcomes.
These models assist healthcare professionals in optimizing the allocation of scarce resources.
Improved patient prognosis is a key benefit of utilizing accurate predictive tools.

Outlook:

Further research and validation of ML models are needed for widespread clinical adoption.
Developing generalizable and interpretable prediction models remains a key challenge.
The future of infectious disease management will likely involve sophisticated AI-driven predictive analytics.