ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2021
Development of a prognostic model for mortality in COVID-19 infection using machine learning.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 2 of them syntheses that pooled it.
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Who cites it
86 citing papers in PubMed, 2 syntheses or guidelines pooled it, 193 citations in OpenAlex.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Association between blood urea nitrogen-to-creatinine ratio trajectories and mortality in patients with severe pneumonia requiring invasive mechanical ventilation: a Medical Information Mart for Intensive Care IV (MIMIC-IV) database analysis.Journal of thoracic disease · 2026Article
- Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity.Scientific reports · 2026Article
- Data mining approach to predicting of death in patients with COVID-19.Journal of education and health promotion · 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
- The Impact of the COVID-19 Pandemic on Osteoporosis Diagnosis and Treatment in Iran: A National Study.International journal of endocrinology and metabolism · 2025Article
- Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis.Scientific reports · 2025Article
- Prediction models based on machine learning algorithms for COVID-19 severity risk.BMC public health · 2025Article
- Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.BMC infectious diseases · 2025Article
- Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies.Biology methods & protocols · 2025Article
- Potential associations of selected polymorphic genetic variants with COVID-19 disease susceptibility and severity.PloS one · 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
- Identifying factors related to mortality of hospitalized COVID-19 patients using machine learning methods.Heliyon · 2024Article
- COVID-19 and silent hypoxemia in a minimal closed-loop model of the respiratory rhythm generator.Biological cybernetics · 2024Article
- A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19.Biotech (Basel (Switzerland)) · 2024Article
- Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.BMC public health · 2024Article
- Improved clinical data imputation via classical and quantum determinantal point processes.eLife · 2024Article
- Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality.PLOS digital health · 2024Article
- Association Between Albumin-Corrected Anion Gap and In-Hospital Mortality and Sepsis-Associated Acute Kidney Injury.Medical science monitor : international medical journal of experimental and clinical research · 2024Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) is a novel disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has quickly risen since the beginning of 2020 to become a global pandemic. As a result of the rapid growth of COVID-19, hospitals are tasked with managing an increasing volume of these cases with neither a known effective therapy, an existing vaccine, nor well-established guidelines for clinical management. The need for actionable knowledge amidst the COVID-19 pandemic is dire and yet, given the urgency of this illness and the speed with which the healthcare workforce must devise useful policies for its management, there is insufficient time to await the conclusions of detailed, controlled, prospective clinical research. Thus, we present a retrospective study evaluating laboratory data and mortality from patients with positive RT-PCR assay results for SARS-CoV-2. The objective of this study is to identify prognostic serum biomarkers in patients at greatest risk of mortality. To this end, we develop a machine learning model using five serum chemistry laboratory parameters (c-reactive protein, blood urea nitrogen, serum calcium, serum albumin, and lactic acid) from 398 patients (43 expired and 355 non-expired) for the prediction of death up to 48 h prior to patient expiration. The resulting support vector machine model achieved 91% sensitivity and 91% specificity (AUC 0.93) for predicting patient expiration status on held-out testing data. Finally, we examine the impact of each feature and feature combination in light of different model predictions, highlighting important patterns of laboratory values that impact outcomes in SARS-CoV-2 infection.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.