{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Parchure P"],"funding":["National Institute of Aging","Division of Cancer Prevention, National Cancer Institute"],"pubmed_abstract":["To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records. A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20-84 hours from the time of prediction. Input features included patients' vital signs, laboratory data and ECG results. Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3-23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%). Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19."],"journal":["BMJ supportive & palliative care"],"pagination":["bmjspcare-2020-002602"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8049537"],"repository":["biostudies-literature"],"pubmed_title":["Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19."],"pmcid":["PMC8049537"],"funding_grant_id":["P30AG028741","P30CA196521"],"pubmed_authors":["Dharmarajan K","Kia A","Freeman R","Reich DL","Mazumdar M","Joshi H","Parchure P","Timsina P"],"additional_accession":[]},"is_claimable":false,"name":"Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19.","description":"To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records. A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20-84 hours from the time of prediction. Input features included patients' vital signs, laboratory data and ECG results. Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3-23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%). Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Sep","modification":"2025-04-18T12:16:21.78Z","creation":"2025-04-06T21:50:13.373Z"},"accession":"S-EPMC8049537","cross_references":{"pubmed":["32963059"],"doi":["10.1136/bmjspcare-2020-002602"]}}