CovidSeniorV1, Main, Exploration, bibRecord, 000983

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

Identifieur interne : 000983 ( Main/Exploration ); précédent : 000982; suivant : 000984

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

Auteurs : David Goodman-Meza [États-Unis] ; Akos Rudas [États-Unis, Hongrie] ; Jeffrey N. Chiang [États-Unis] ; Paul C. Adamson [États-Unis] ; Joseph Ebinger [États-Unis] ; Nancy Sun [États-Unis] ; Patrick Botting [États-Unis] ; Jennifer A. Fulcher [États-Unis] ; Faysal G. Saab [États-Unis] ; Rachel Brook [États-Unis] ; Eleazar Eskin [États-Unis] ; Ulzee An [États-Unis] ; Misagh Kordi [États-Unis] ; Brandon Jew [États-Unis] ; Brunilda Balliu [États-Unis] ; Zeyuan Chen [États-Unis] ; Brian L. Hill [États-Unis] ; Elior Rahmani [États-Unis] ; Eran Halperin [États-Unis] ; Vladimir Manuel [États-Unis]

Source :

PloS one [ 1932-6203 ] ; 2020.

RBID : pubmed:32960917

Descripteurs français

English descriptors

KwdEn :
- Adult (MeSH), Aged (MeSH), Area Under Curve (MeSH), Betacoronavirus (MeSH), Clinical Laboratory Techniques (methods), Clinical Laboratory Techniques (standards), Coronavirus Infections (diagnosis), Humans (MeSH), Inpatients (MeSH), Los Angeles (MeSH), Machine Learning (MeSH), Mass Screening (methods), Mass Screening (standards), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (diagnosis), Polymerase Chain Reaction (MeSH), Retrospective Studies (MeSH).
MESH :
- geographic : Los Angeles.
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- methods : Clinical Laboratory Techniques, Mass Screening.
- standards : Clinical Laboratory Techniques, Mass Screening.
- Adult, Aged, Area Under Curve, Betacoronavirus, Humans, Inpatients, Machine Learning, Middle Aged, Pandemics, Polymerase Chain Reaction, Retrospective Studies.

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.

DOI: 10.1371/journal.pone.0239474
PubMed: 32960917
PubMed Central: PMC7508387

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000136
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Le document en format XML

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<author><name sortKey="Balliu, Brunilda" sort="Balliu, Brunilda" uniqKey="Balliu B" first="Brunilda" last="Balliu">Brunilda Balliu</name>
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<author><name sortKey="Chen, Zeyuan" sort="Chen, Zeyuan" uniqKey="Chen Z" first="Zeyuan" last="Chen">Zeyuan Chen</name>
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<author><name sortKey="Hill, Brian L" sort="Hill, Brian L" uniqKey="Hill B" first="Brian L" last="Hill">Brian L. Hill</name>
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<author><name sortKey="Rahmani, Elior" sort="Rahmani, Elior" uniqKey="Rahmani E" first="Elior" last="Rahmani">Elior Rahmani</name>
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<author><name sortKey="Manuel, Vladimir" sort="Manuel, Vladimir" uniqKey="Manuel V" first="Vladimir" last="Manuel">Vladimir Manuel</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.</title>
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<author><name sortKey="Rudas, Akos" sort="Rudas, Akos" uniqKey="Rudas A" first="Akos" last="Rudas">Akos Rudas</name>
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<author><name sortKey="Sun, Nancy" sort="Sun, Nancy" uniqKey="Sun N" first="Nancy" last="Sun">Nancy Sun</name>
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<author><name sortKey="Botting, Patrick" sort="Botting, Patrick" uniqKey="Botting P" first="Patrick" last="Botting">Patrick Botting</name>
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<wicri:regionArea>Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California</wicri:regionArea>
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</placeName>
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<author><name sortKey="Brook, Rachel" sort="Brook, Rachel" uniqKey="Brook R" first="Rachel" last="Brook">Rachel Brook</name>
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<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California</wicri:regionArea>
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<author><name sortKey="Eskin, Eleazar" sort="Eskin, Eleazar" uniqKey="Eskin E" first="Eleazar" last="Eskin">Eleazar Eskin</name>
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<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Computational Medicine, UCLA, Los Angeles, California</wicri:regionArea>
<placeName><region type="state">Californie</region>
</placeName>
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<affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science, UCLA, Los Angeles, California, United States of America.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Computer Science, UCLA, Los Angeles, California</wicri:regionArea>
<placeName><region type="state">Californie</region>
</placeName>
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<affiliation wicri:level="2"><nlm:affiliation>Department of Human Genetics, UCLA, Los Angeles, California, United States of America.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Human Genetics, UCLA, Los Angeles, California</wicri:regionArea>
<placeName><region type="state">Californie</region>
</placeName>
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<author><name sortKey="An, Ulzee" sort="An, Ulzee" uniqKey="An U" first="Ulzee" last="An">Ulzee An</name>
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<country xml:lang="fr">États-Unis</country>
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<country xml:lang="fr">États-Unis</country>
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<author><name sortKey="Chen, Zeyuan" sort="Chen, Zeyuan" uniqKey="Chen Z" first="Zeyuan" last="Chen">Zeyuan Chen</name>
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<country xml:lang="fr">États-Unis</country>
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<author><name sortKey="Hill, Brian L" sort="Hill, Brian L" uniqKey="Hill B" first="Brian L" last="Hill">Brian L. Hill</name>
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<wicri:regionArea>Department of Computer Science, UCLA, Los Angeles, California</wicri:regionArea>
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<author><name sortKey="Rahmani, Elior" sort="Rahmani, Elior" uniqKey="Rahmani E" first="Elior" last="Rahmani">Elior Rahmani</name>
<affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science, UCLA, Los Angeles, California, United States of America.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
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<author><name sortKey="Halperin, Eran" sort="Halperin, Eran" uniqKey="Halperin E" first="Eran" last="Halperin">Eran Halperin</name>
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<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Computational Medicine, UCLA, Los Angeles, California</wicri:regionArea>
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<affiliation wicri:level="2"><nlm:affiliation>Department of Human Genetics, UCLA, Los Angeles, California, United States of America.</nlm:affiliation>
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<wicri:regionArea>Department of Human Genetics, UCLA, Los Angeles, California</wicri:regionArea>
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</placeName>
</affiliation>
<affiliation wicri:level="2"><nlm:affiliation>Department of Anesthesiology, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Anesthesiology, David Geffen School of Medicine at UCLA, Los Angeles, California</wicri:regionArea>
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</placeName>
</affiliation>
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<author><name sortKey="Manuel, Vladimir" sort="Manuel, Vladimir" uniqKey="Manuel V" first="Vladimir" last="Manuel">Vladimir Manuel</name>
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</placeName>
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<series><title level="j">PloS one</title>
<idno type="eISSN">1932-6203</idno>
<imprint><date when="2020" type="published">2020</date>
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<term>Aged (MeSH)</term>
<term>Area Under Curve (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Clinical Laboratory Techniques (methods)</term>
<term>Clinical Laboratory Techniques (standards)</term>
<term>Coronavirus Infections (diagnosis)</term>
<term>Humans (MeSH)</term>
<term>Inpatients (MeSH)</term>
<term>Los Angeles (MeSH)</term>
<term>Machine Learning (MeSH)</term>
<term>Mass Screening (methods)</term>
<term>Mass Screening (standards)</term>
<term>Middle Aged (MeSH)</term>
<term>Pandemics (MeSH)</term>
<term>Pneumonia, Viral (diagnosis)</term>
<term>Polymerase Chain Reaction (MeSH)</term>
<term>Retrospective Studies (MeSH)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term>
<term>Adulte d'âge moyen (MeSH)</term>
<term>Aire sous la courbe (MeSH)</term>
<term>Apprentissage machine (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Dépistage de masse (méthodes)</term>
<term>Dépistage de masse (normes)</term>
<term>Humains (MeSH)</term>
<term>Infections à coronavirus (diagnostic)</term>
<term>Los Angeles (MeSH)</term>
<term>Pandémies (MeSH)</term>
<term>Patients hospitalisés (MeSH)</term>
<term>Pneumopathie virale (diagnostic)</term>
<term>Réaction de polymérisation en chaîne (MeSH)</term>
<term>Sujet âgé (MeSH)</term>
<term>Techniques de laboratoire clinique (méthodes)</term>
<term>Techniques de laboratoire clinique (normes)</term>
<term>Études rétrospectives (MeSH)</term>
</keywords>
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</keywords>
<keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Clinical Laboratory Techniques</term>
<term>Mass Screening</term>
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<term>Techniques de laboratoire clinique</term>
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<term>Techniques de laboratoire clinique</term>
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<term>Mass Screening</term>
</keywords>
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<term>Aged</term>
<term>Area Under Curve</term>
<term>Betacoronavirus</term>
<term>Humans</term>
<term>Inpatients</term>
<term>Machine Learning</term>
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<term>Pandémies</term>
<term>Patients hospitalisés</term>
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<front><div type="abstract" xml:lang="en">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.</div>
</front>
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<DateCompleted><Year>2020</Year>
<Month>10</Month>
<Day>13</Day>
</DateCompleted>
<DateRevised><Year>2020</Year>
<Month>10</Month>
<Day>13</Day>
</DateRevised>
<Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>15</Volume>
<Issue>9</Issue>
<PubDate><Year>2020</Year>
</PubDate>
</JournalIssue>
<Title>PloS one</Title>
<ISOAbbreviation>PLoS One</ISOAbbreviation>
</Journal>
<ArticleTitle>A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.</ArticleTitle>
<Pagination><MedlinePgn>e0239474</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0239474</ELocationID>
<Abstract><AbstractText>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.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Goodman-Meza</LastName>
<ForeName>David</ForeName>
<Initials>D</Initials>
<Identifier Source="ORCID">0000-0001-9382-3564</Identifier>
<AffiliationInfo><Affiliation>Division of Infectious Diseases, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Rudas</LastName>
<ForeName>Akos</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary.</Affiliation>
</AffiliationInfo>
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<ForeName>Jeffrey N</ForeName>
<Initials>JN</Initials>
<Identifier Source="ORCID">0000-0002-6843-1355</Identifier>
<AffiliationInfo><Affiliation>Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Adamson</LastName>
<ForeName>Paul C</ForeName>
<Initials>PC</Initials>
<Identifier Source="ORCID">0000-0002-2776-6458</Identifier>
<AffiliationInfo><Affiliation>Division of Infectious Diseases, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
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<ForeName>Joseph</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Sun</LastName>
<ForeName>Nancy</ForeName>
<Initials>N</Initials>
<AffiliationInfo><Affiliation>Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Botting</LastName>
<ForeName>Patrick</ForeName>
<Initials>P</Initials>
<AffiliationInfo><Affiliation>Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Fulcher</LastName>
<ForeName>Jennifer A</ForeName>
<Initials>JA</Initials>
<Identifier Source="ORCID">0000-0001-9895-8636</Identifier>
<AffiliationInfo><Affiliation>Division of Infectious Diseases, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Saab</LastName>
<ForeName>Faysal G</ForeName>
<Initials>FG</Initials>
<Identifier Source="ORCID">0000-0002-4568-3613</Identifier>
<AffiliationInfo><Affiliation>Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Brook</LastName>
<ForeName>Rachel</ForeName>
<Initials>R</Initials>
<AffiliationInfo><Affiliation>Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Eskin</LastName>
<ForeName>Eleazar</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Computer Science, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Human Genetics, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
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<ForeName>Ulzee</ForeName>
<Initials>U</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Kordi</LastName>
<ForeName>Misagh</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Jew</LastName>
<ForeName>Brandon</ForeName>
<Initials>B</Initials>
<AffiliationInfo><Affiliation>Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Balliu</LastName>
<ForeName>Brunilda</ForeName>
<Initials>B</Initials>
<AffiliationInfo><Affiliation>Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Chen</LastName>
<ForeName>Zeyuan</ForeName>
<Initials>Z</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Hill</LastName>
<ForeName>Brian L</ForeName>
<Initials>BL</Initials>
<Identifier Source="ORCID">0000-0002-6881-5770</Identifier>
<AffiliationInfo><Affiliation>Department of Computer Science, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Rahmani</LastName>
<ForeName>Elior</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Halperin</LastName>
<ForeName>Eran</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Computer Science, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Human Genetics, UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Anesthesiology, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Manuel</LastName>
<ForeName>Vladimir</ForeName>
<Initials>V</Initials>
<AffiliationInfo><Affiliation>Faculty Practice Group, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>UCLA Clinical and Translational Science Institute, Los Angeles, California, United States of America.</Affiliation>
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<Month>09</Month>
<Day>22</Day>
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</MeshHeading>
<MeshHeading><DescriptorName UI="D007297" MajorTopicYN="Y">Inpatients</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D015141" MajorTopicYN="N" Type="Geographic">Los Angeles</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="Y">Machine Learning</DescriptorName>
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<QualifierName UI="Q000592" MajorTopicYN="N">standards</QualifierName>
</MeshHeading>
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</MeshHeading>
<MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName>
<QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016133" MajorTopicYN="N">Polymerase Chain Reaction</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName>
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<CoiStatement>No authors have competing interests.</CoiStatement>
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<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year>
<Month>06</Month>
<Day>30</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2020</Year>
<Month>09</Month>
<Day>01</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2020</Year>
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	Serveur d'exploration sur la COVID chez les séniors
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Serveur d'exploration sur la COVID chez les séniors

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

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

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