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Updated: Sep 22, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Published on: May 15, 2020

Predictive modeling for COVID-19 readmission risk using machine learning algorithms.

Mostafa Shanbehzadeh¹, Azita Yazdani², Mohsen Shafiee³

¹Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.

BMC Medical Informatics and Decision Making

|May 20, 2022

Summary

This summary is machine-generated.

Machine learning accurately predicts COVID-19 readmission risk, aiding hospital resource allocation. The Water Wave Optimization algorithm demonstrated superior performance in identifying high-risk patients for better care management.

Keywords:

COVID-19 Data mining Machine learning Patient readmission

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Public Health

Background:

The COVID-19 pandemic severely strained healthcare systems globally.
Hospital resource limitations, including ICU beds and ventilators, necessitated strategies to manage patient flow.
Reducing COVID-19 readmissions emerged as a critical approach to preserve hospital capacity.

Purpose of the Study:

To develop and evaluate machine learning (ML) models for predicting COVID-19 patient readmission risk.
To identify the most effective ML algorithm for accurate readmission prediction.
To support optimal allocation of limited healthcare resources.

Main Methods:

A retrospective analysis of 1225 COVID-19 patients discharged between January 2020 and October 2021.
Feature selection using horse herd optimization algorithms.
Development and comparison of four ML models: decision tree, support vector machine, k-nearest neighbors, and a hybrid Water Wave Optimization (WWO) with neural network.

Main Results:

The Water Wave Optimization (WWO) algorithm achieved the highest predictive performance.
WWO demonstrated excellent results in tenfold cross-validation: accuracy (0.9705), precision (0.9729), recall (0.9869), and F-measure (0.9795).
Seventeen validated features were used to train the ML algorithms.

Conclusions:

The WWO algorithm significantly outperforms other ML methods in predicting COVID-19 readmission risk.
These predictive models can guide clinicians and policymakers in resource management.
Accurate readmission risk prediction facilitates the optimal allocation of scarce hospital resources to critically ill COVID-19 patients.