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...

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Published inPloS one Vol. 15; no. 9; p. e0239474
Main Authors 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
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 22.09.2020
Public Library of Science (PLoS)
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Abstract 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.
AbstractList 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.
Audience Academic
Author Halperin, Eran
Brook, Rachel
Adamson, Paul C
Saab, Faysal G
Balliu, Brunilda
Chen, Zeyuan
Sun, Nancy
Eskin, Eleazar
An, Ulzee
Chiang, Jeffrey N
Kordi, Misagh
Hill, Brian L
Manuel, Vladimir
Rudas, Akos
Rahmani, Elior
Jew, Brandon
Fulcher, Jennifer A
Goodman-Meza, David
Ebinger, Joseph
Botting, Patrick
AuthorAffiliation 3 Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary
4 Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
7 Department of Human Genetics, UCLA, Los Angeles, California, United States of America
6 Department of Computer Science, UCLA, Los Angeles, California, United States of America
5 Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
9 Faculty Practice Group, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
University of Pennsylvania, UNITED STATES
1 Division of Infectious Diseases, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
8 Department of Anesthesiology, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
10 UCLA Clinical and Translational Science Institute, Los Angeles, California, United States of America
2 Department of Com
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ContentType Journal Article
Copyright COPYRIGHT 2020 Public Library of Science
2020 Goodman-Meza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2020 Goodman-Meza et al 2020 Goodman-Meza et al
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– notice: 2020 Goodman-Meza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: 2020 Goodman-Meza et al 2020 Goodman-Meza et al
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Issue 9
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Creative Commons Attribution License
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Notes Competing Interests: No authors have competing interests.
These authors jointly supervised the work.
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F Zhou (pone.0239474.ref008) 2020; 395
L Wynants (pone.0239474.ref015) 2020; 369
R. Service (pone.0239474.ref007) 2020
AFdM Batista (pone.0239474.ref013) 2020
MP Cheng (pone.0239474.ref004) 2020
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S Ward (pone.0239474.ref005) 2020
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MP Cheng (pone.0239474.ref003) 2020; 172
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PM Bossuyt (pone.0239474.ref017) 2015; 351
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RelatedPersons Geffen, David
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Snippet 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...
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SubjectTerms Adult
Aged
Algorithms
Area Under Curve
Betacoronavirus
Biological markers
Biology and life sciences
Blood
Cardiology
Clinical Laboratory Techniques - methods
Clinical Laboratory Techniques - standards
Computer and Information Sciences
Computer science
Confidence intervals
Coronavirus Infections - diagnosis
Coronaviruses
COVID-19
COVID-19 Testing
Diagnosis
Diagnostic systems
Emergency medical care
Emergency medical services
Geffen, David
Health aspects
Hospitals
Humans
Identification and classification
Infectious diseases
Inpatients
Laboratories
Learning
Learning algorithms
Los Angeles
Lymphocytes
Machine Learning
Mass Screening - methods
Mass Screening - standards
Medical diagnosis
Medicine
Medicine and Health Sciences
Middle Aged
Neutrophils
Pandemics
Physical Sciences
Pneumonia, Viral - diagnosis
Polymerase Chain Reaction
Proteins
Public health
Research and Analysis Methods
Retrospective Studies
SARS-CoV-2
Severe acute respiratory syndrome
Severe acute respiratory syndrome coronavirus 2
Standard deviation
Supervision
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Title A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity
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