Cargando…

A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity

Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic an...

Descripción completa

Detalles Bibliográficos
Autores principales: Goodman-Meza, David, Rudas, Akos, Chiang, Jeffrey N., Adamson, Paul C., Ebinger, Joseph, Sun, Nancy, Botting, Patrick, Fulcher, Jennifer A., Saab, Faysal G., Brook, Rachel, Eskin, Eleazar, An, Ulzee, Kordi, Misagh, Jew, Brandon, Balliu, Brunilda, Chen, Zeyuan, Hill, Brian L., Rahmani, Elior, Halperin, Eran, Manuel, Vladimir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508387/
https://www.ncbi.nlm.nih.gov/pubmed/32960917
http://dx.doi.org/10.1371/journal.pone.0239474
_version_ 1783585410414280704
author Goodman-Meza, David
Rudas, Akos
Chiang, Jeffrey N.
Adamson, Paul C.
Ebinger, Joseph
Sun, Nancy
Botting, Patrick
Fulcher, Jennifer A.
Saab, Faysal G.
Brook, Rachel
Eskin, Eleazar
An, Ulzee
Kordi, Misagh
Jew, Brandon
Balliu, Brunilda
Chen, Zeyuan
Hill, Brian L.
Rahmani, Elior
Halperin, Eran
Manuel, Vladimir
author_facet Goodman-Meza, David
Rudas, Akos
Chiang, Jeffrey N.
Adamson, Paul C.
Ebinger, Joseph
Sun, Nancy
Botting, Patrick
Fulcher, Jennifer A.
Saab, Faysal G.
Brook, Rachel
Eskin, Eleazar
An, Ulzee
Kordi, Misagh
Jew, Brandon
Balliu, Brunilda
Chen, Zeyuan
Hill, Brian L.
Rahmani, Elior
Halperin, Eran
Manuel, Vladimir
author_sort Goodman-Meza, David
collection PubMed
description Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87–0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85–0.98), specificity of 0.64 (95% CI 0.58–0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable.
format Online
Article
Text
id pubmed-7508387
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-75083872020-10-01 A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity Goodman-Meza, David Rudas, Akos Chiang, Jeffrey N. Adamson, Paul C. Ebinger, Joseph Sun, Nancy Botting, Patrick Fulcher, Jennifer A. Saab, Faysal G. Brook, Rachel Eskin, Eleazar An, Ulzee Kordi, Misagh Jew, Brandon Balliu, Brunilda Chen, Zeyuan Hill, Brian L. Rahmani, Elior Halperin, Eran Manuel, Vladimir PLoS One Research Article Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87–0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85–0.98), specificity of 0.64 (95% CI 0.58–0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable. Public Library of Science 2020-09-22 /pmc/articles/PMC7508387/ /pubmed/32960917 http://dx.doi.org/10.1371/journal.pone.0239474 Text en © 2020 Goodman-Meza et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Goodman-Meza, David
Rudas, Akos
Chiang, Jeffrey N.
Adamson, Paul C.
Ebinger, Joseph
Sun, Nancy
Botting, Patrick
Fulcher, Jennifer A.
Saab, Faysal G.
Brook, Rachel
Eskin, Eleazar
An, Ulzee
Kordi, Misagh
Jew, Brandon
Balliu, Brunilda
Chen, Zeyuan
Hill, Brian L.
Rahmani, Elior
Halperin, Eran
Manuel, Vladimir
A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity
title A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity
title_full A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity
title_fullStr A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity
title_full_unstemmed A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity
title_short A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity
title_sort machine learning algorithm to increase covid-19 inpatient diagnostic capacity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508387/
https://www.ncbi.nlm.nih.gov/pubmed/32960917
http://dx.doi.org/10.1371/journal.pone.0239474
work_keys_str_mv AT goodmanmezadavid amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT rudasakos amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT chiangjeffreyn amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT adamsonpaulc amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT ebingerjoseph amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT sunnancy amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT bottingpatrick amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT fulcherjennifera amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT saabfaysalg amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT brookrachel amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT eskineleazar amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT anulzee amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT kordimisagh amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT jewbrandon amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT balliubrunilda amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT chenzeyuan amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT hillbrianl amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT rahmanielior amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT halperineran amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT manuelvladimir amachinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT goodmanmezadavid machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT rudasakos machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT chiangjeffreyn machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT adamsonpaulc machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT ebingerjoseph machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT sunnancy machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT bottingpatrick machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT fulcherjennifera machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT saabfaysalg machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT brookrachel machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT eskineleazar machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT anulzee machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT kordimisagh machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT jewbrandon machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT balliubrunilda machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT chenzeyuan machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT hillbrianl machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT rahmanielior machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT halperineran machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity
AT manuelvladimir machinelearningalgorithmtoincreasecovid19inpatientdiagnosticcapacity