Cited by 104Machine Learning in HealthcareArtificial Intelligence in Healthcare and EducationCOVID-19 diagnosis using AI

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Machine learning to assist clinical decision-making during the COVID-19 pandemic

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Debnath, Shubham, Barnaby, Douglas P., Coppa, Kevin, Makhnevich, Alexander, Kim, Eun Ji, Chatterjee, Saurav, Tóth, Viktor, Levy, Todd J., Paradis, Marc d., Cohen, Stuart L., Hirsch, Jamie S., Zanos, Theodoros P., the Northwell COVID-19 Research Consortium, Becker, Lance B., Cookingham, Jennifer, Davidson, Karina W., Dominello, Andrew J., Falzon, Louise, McGinn, Thomas, Mogavero, Jazmin N., Osorio, Gabrielle A.·2020·Bioelectronic Medicine

DOI 10.1186/s42234-020-00050-8PMC PMC7347420

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Abstract

Background: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for “Emergency ML.” Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.

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Fig. 1 a ML/AI in the patient care pathway. The black asterisks represent multiple decision points during the patient care pathway that could be augmented by ML/AI tools. The green traces represent a COVID-19 negative diagnosis or recovery while the orange and red traces represent risk stratification of patients by lower and higher risks of deterioration, respectively, as determined by a potential ML/AI model. Additional decisions in the hospital include prioritization of care, allocation of resources, and estimation of prognosis. b An expanding COVID-19 database. Since March 1, 2020, there has been an increasing amount of COVID-19 patient data, shown here by new admissions and new medical data entries at Northwell Health, New York’s largest health system, facilities. Given increasing hospital admissions (black trace, left y-axis), there have been hundreds of thousands of new data entries per day (colored bars, right y-axis), including vitals, laboratory results, medication orders, and patient comorbidities. This vast data allows a unique opportunity to implement ML/AI to support medical frontlines and healthcare administrators in the fight against COVID-19. c Evolving patient profiles and discharge rates. Basic characteristics of the patient population changed during the progression of the wave of new cases, which can affect performance of a predictive model. For example, the average length of stay for expired patients and those discharged alive (dark blue with square markers and light blue with triangle markers, respectively, left y-axis) diverged in mid-April. Because of these changes, a predictive model with good early performance may decline because of differences between patients hospitalized for three weeks compared to less than a week. Also, an individual’s patient profile may have evolved significantly from hospital admission to those timepoints later during hospitalization. Discharges per day (grey bars, right y-axis) increased with the pandemic’s peak and declined with reduced numbers of new casesCC BY · PubMed Central

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Authors: Debnath, S., Barnaby, D. P., Coppa, K., Makhnevich, A., Kim, E. J., Chatterjee, S., Tóth, V., Levy, T. J., Paradis, M. D., Cohen, S. L., Hirsch, J. S., Zanos, T. P., the Northwell COVID-19 Research Consortium, Becker, L. B., Cookingham, J., Davidson, K. W., Dominello, A. J., Falzon, L., McGinn, T., Mogavero, J. N., & Osorio, G. A.
Affiliations: Northwell Health (US)
Corresponding author: Zanos, Theodoros P.
ORCID: Debnath, Shubham
Barnaby, Douglas P.
Coppa, Kevin
Kim, Eun Ji
Chatterjee, Saurav
Tóth, Viktor
Levy, Todd J.
Paradis, Marc d.
Cohen, Stuart L.
Hirsch, Jamie S.
Zanos, Theodoros P.
Becker, Lance B.
Davidson, Karina W.
Dominello, Andrew J.
Falzon, Louise
McGinn, Thomas
Mogavero, Jazmin N.
Venue: Bioelectronic Medicine
Publisher: Springer Science and Business Media LLC
Published: 2020-07-10
Volume: 6
Issue: 1
Pages: 14-14
ISSN: 2332-8886 (ISSN-L)
DOI: 10.1186/s42234-020-00050-8
PMC: PMC7347420
OpenAlex: W3042081405
Cited by: 104
References: 57
Citations by year: 2026: 3 · 2025: 10 · 2024: 12 · 2023: 17 · 2022: 29 · 2021: 27 · 2020: 6
FWCI: 7.76
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Bioelectronic Medicine (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Institute on Aging (R24AG064191)
U.S. National Library of Medicine (R01LM012836)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Keywords: COVID-19 pandemic, machine learning, clinical decision-making, clinical prediction model, model validation, hospital resource allocation, health care administrators, care pathways

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Machine learning to assist clinical decision-making during the COVID-19 pandemic

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