Related Experiment Video
Updated: Jul 26, 2025
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on
Parastoo Amiri1, Mahdieh Montazeri2, Fahimeh Ghasemian3
1Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran.
Insights
Machine learning accurately predicts COVID-19 mortality risk and length of stay in patients with chronic conditions. These algorithms aid early intervention for better patient outcomes.
Area of Science:
- Medical Informatics
- Computational Biology
- Epidemiology
Background:
- COVID-19 poses a higher mortality risk for patients with chronic comorbidities.
- Machine learning (ML) offers a potential solution for early and rapid clinical evaluation of disease severity.
- Resource allocation and prioritization can be improved by accurately assessing COVID-19 severity.
Purpose of the Study:
- To predict mortality risk in COVID-19 patients with chronic comorbidities using ML algorithms.
- To predict length of stay (LoS) for COVID-19 patients with chronic comorbidities using ML algorithms.
- To identify key factors influencing mortality and LoS in this patient cohort.
Main Methods:
- Retrospective study of 1291 COVID-19 patients with chronic comorbidities (March 2020-January 2021).
- Utilized feature scoring and established ML algorithms (including Ensemble Learning) to predict mortality and LoS.
- Evaluated model performance using F1, precision, recall, and accuracy metrics, adhering to TRIPOD guidelines.
Main Results:
- Gradient boosting achieved 84.15% accuracy in predicting mortality risk.
- Multilayer Perceptron (MLP) was optimal for predicting LoS (MSE = 38.96).
- Key predictors for mortality included hyperlipidemia, diabetes, asthma, and cancer; shortness of breath was key for LoS.
Conclusions:
- ML algorithms effectively predict mortality risk and LoS in COVID-19 patients with chronic comorbidities.
- Gradient boosting and MLP models can rapidly identify high-risk patients for timely intervention.
- Utilizing patient data, ML aids physicians in managing patients with complex health conditions.
Background:
The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality.
Objective:
The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms.
Methods:
This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting.
Results:
This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath.
Conclusion:
The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions.
More Related Videos
05:15Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
Related Concept Videos
01:21Cancer Survival Analysis
01:18Steps in Outbreak Investigation
01:20Comparing the Survival Analysis of Two or More Groups
01:29Chronic Obstructive Pulmonary Disease-V: Management
Smoking Cessation
01:22Chronic Obstructive Pulmonary Disease
Smoking is a primary risk factor for COPD, with over 80% of patients having a history of it. Patients typically experience progressive dyspnea or labored breathing, frequent coughing, and recurrent pulmonary infections. Many eventually succumb to respiratory failure, characterized by...
01:17Classification of Illness
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...