ReviewMedical & biological engineering & computing2022
A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients.
Review in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Tribulations, Triumphs, and Governance: Shaping the Future of Artificial Intelligence in Healthcare.EJIFCC · 2025Article
- IoT-based bed and ventilator management system during the COVID-19 pandemic.Scientific reports · 2025Article
- Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).Scientific reports · 2025Article
- Machine Learning-Based Prediction of In-Hospital Mortality in Severe COVID-19 Patients Using Hematological Markers.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2025Article
- A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.BMC medical informatics and decision making · 2024Article
- Predicting Intensive Care Unit Admission in COVID-19-Infected Pregnant Women Using Machine Learning.Journal of clinical medicine · 2024Article
- An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19.Scientific reports · 2024Article
- Analyzing and predicting short-term substance use behaviors of persons who use drugs in the great plains of the U.S.PloS one · 2024Article
- Hematopathology of Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Coronavirus Disease-19.Surgical pathology clinics · 2023Review
- Clinical prediction rules for adverse evolution in patients with COVID-19 by the Omicron variant.International journal of medical informatics · 2023Article
- Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study.Journal of business research · 2023Article
- Machine learning models for predicting severe COVID-19 outcomes in hospitals.Informatics in medicine unlocked · 2023Article
- Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records.International journal of medical informatics · 2022Observational
- Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil.International journal of medical informatics · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this article, we discuss the development of prognostic machine learning (ML) models for COVID-19 progression, by focusing on the task of predicting ICU admission within (any of) the next 5 days. On the basis of 6,625 complete blood count (CBC) tests from 1,004 patients, of which 18% were admitted to intensive care unit (ICU), we created four ML models, by adopting a robust development procedure which was designed to minimize risks of bias and over-fitting, according to reference guidelines. The best model, a support vector machine, had an AUC of .85, a Brier score of .14, and a standardized net benefit of .69: these scores indicate that the model performed well over a variety of prediction criteria. We also conducted an interpretability study to back up our findings, showing that the data on which the developed model is based is consistent with the current medical literature. This also demonstrates that CBC data and ML methods can be used to predict COVID-19 patients' ICU admission at a relatively low cost: in particular, since CBC data can be quickly obtained by means of routine blood exams, our models could be used in resource-constrained settings and provide health practitioners with rapid and reliable indications.
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