Machine learning to assist clinical decision-making during the COVID-19 pandemic
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 ha...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-07-01
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| Series: | Bioelectronic Medicine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s42234-020-00050-8 |
| _version_ | 1818883646216470528 |
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| author | Shubham Debnath Douglas P. Barnaby Kevin Coppa Alexander Makhnevich Eun Ji Kim Saurav Chatterjee Viktor Tóth Todd J. Levy Marc d. Paradis Stuart L. Cohen Jamie S. Hirsch Theodoros P. Zanos the Northwell COVID-19 Research Consortium |
| author_facet | Shubham Debnath Douglas P. Barnaby Kevin Coppa Alexander Makhnevich Eun Ji Kim Saurav Chatterjee Viktor Tóth Todd J. Levy Marc d. Paradis Stuart L. Cohen Jamie S. Hirsch Theodoros P. Zanos the Northwell COVID-19 Research Consortium |
| author_sort | Shubham Debnath |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T15:36:57Z |
| format | Article |
| id | doaj.art-b103d57c78064a208c97bafb534794cc |
| institution | Directory Open Access Journal |
| issn | 2332-8886 |
| language | English |
| last_indexed | 2024-12-19T15:36:57Z |
| publishDate | 2020-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Bioelectronic Medicine |
| spelling | doaj.art-b103d57c78064a208c97bafb534794cc2022-12-21T20:15:34ZengBMCBioelectronic Medicine2332-88862020-07-01611810.1186/s42234-020-00050-8Machine learning to assist clinical decision-making during the COVID-19 pandemicShubham Debnath0Douglas P. Barnaby1Kevin Coppa2Alexander Makhnevich3Eun Ji Kim4Saurav Chatterjee5Viktor Tóth6Todd J. Levy7Marc d. Paradis8Stuart L. Cohen9Jamie S. Hirsch10Theodoros P. Zanos11the Northwell COVID-19 Research ConsortiumInstitute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell HealthInstitute of Health Innovations and Outcomes Research, Feinstein Institutes for Medical Research, Northwell HealthDepartment of Information Services, Northwell HealthDonald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell HealthInstitute of Health Innovations and Outcomes Research, Feinstein Institutes for Medical Research, Northwell HealthCardiology, Long Island Jewish Medical Center and Feinstein Institutes for Medical Research, Northwell HealthInstitute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell HealthInstitute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell HealthHoldings and Ventures, Northwell HealthInstitute of Health Innovations and Outcomes Research, Feinstein Institutes for Medical Research, Northwell HealthDonald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell HealthInstitute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell HealthAbstract 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.http://link.springer.com/article/10.1186/s42234-020-00050-8Artificial intelligence (AI)Clinical decision-makingCoronavirus disease 19 (COVID-19)HealthcareMachine learning (ML) |
| spellingShingle | Shubham Debnath Douglas P. Barnaby Kevin Coppa Alexander Makhnevich Eun Ji Kim Saurav Chatterjee Viktor Tóth Todd J. Levy Marc d. Paradis Stuart L. Cohen Jamie S. Hirsch Theodoros P. Zanos the Northwell COVID-19 Research Consortium Machine learning to assist clinical decision-making during the COVID-19 pandemic Bioelectronic Medicine Artificial intelligence (AI) Clinical decision-making Coronavirus disease 19 (COVID-19) Healthcare Machine learning (ML) |
| title | Machine learning to assist clinical decision-making during the COVID-19 pandemic |
| title_full | Machine learning to assist clinical decision-making during the COVID-19 pandemic |
| title_fullStr | Machine learning to assist clinical decision-making during the COVID-19 pandemic |
| title_full_unstemmed | Machine learning to assist clinical decision-making during the COVID-19 pandemic |
| title_short | Machine learning to assist clinical decision-making during the COVID-19 pandemic |
| title_sort | machine learning to assist clinical decision making during the covid 19 pandemic |
| topic | Artificial intelligence (AI) Clinical decision-making Coronavirus disease 19 (COVID-19) Healthcare Machine learning (ML) |
| url | http://link.springer.com/article/10.1186/s42234-020-00050-8 |
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