ArticleRisk management and healthcare policy2021
Machine Learning Algorithms are Superior to Conventional Regression Models in Predicting Risk Stratification of COVID-19 Patients.
Article in Risk management and healthcare policy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- A novel interpretable machine learning framework for predicting postpartum depression: a SHAP-based analysis of maternal and infant health indicators.Frontiers in psychiatry · 2026Article
- Machine Learning Model Predicts New-Onset Lower Extremity Deep Vein Thrombosis After Pelvic Fracture Surgery and Targeted Diagnosis.Clinical epidemiology · 2026Article
- How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics.Risk analysis : an official publication of the Society for Risk Analysis · 2025Review
- Machine learning vs. traditional logistic regression: predictive performance and risk factor identification for child nutritional outcome in Pakistan.BMC public health · 2025Article
- Spatio-temporal forecasting of COVID-19 cases in the Netherlands for source and contact tracing.International journal of population data science · 2025Article
- Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma.BMC gastroenterology · 2024Article
- Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms.BMC medical informatics and decision making · 2024Article
- Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts.Sensors (Basel, Switzerland) · 2022Article
- Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.Frontiers in digital health · 2021Article
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3 authors.
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Abstract
backgroundIt is very important to determine the risk of patients developing severe or critical COVID-19, but most of the existing risk prediction models are established using conventional regression models. We aim to use machine learning algorithms to develop predictive models and compare predictive performance with logistic regression models.
methodsThe medical record of 161 COVID-19 patients who were diagnosed January-April 2020 were retrospectively analyzed. The patients were divided into two groups: asymptomatic-moderate group (132 cases) and severe or above group (29 cases). The clinical features and laboratory biomarkers of these two groups were compared. Machine learning algorithms and multivariate logistic regression analysis were used to construct two COVID-19 risk stratification prediction models, and the area under the curve (AUC) was used to compare the predictive efficacy of these two models.
resultsA machine learning model was constructed based on seven characteristic variables: high sensitivity C-reactive protein (hs-CRP), procalcitonin (PCT), age, neutrophil count (Neuc), hemoglobin (HGB), percentage of neutrophils (Neur), and platelet distribution width (PDW). The AUC of the model was 0.978 (95% CI: 0.960-0.996), which was significantly higher than that of the logistic regression model (0.827; 95% CI: 0.724-0.930) (
conclusionMachine learning algorithms improve the accuracy of risk stratification in patients with COVID-19. Using detection algorithms derived from these techniques can enhance the identification of critically ill patients.
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