Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation.
Identifieur interne : 000893 ( Main/Corpus ); précédent : 000892; suivant : 000894Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation.
Auteurs : Ahmed Abdulaal ; Aatish Patel ; Esmita Charani ; Sarah Denny ; Nabeela Mughal ; Luke MooreSource :
- Journal of medical Internet research [ 1438-8871 ] ; 2020.
English descriptors
- KwdEn :
- Aged (MeSH), Aged, 80 and over (MeSH), Artificial Intelligence (MeSH), Betacoronavirus (MeSH), Comorbidity (MeSH), Coronavirus Infections (diagnosis), Coronavirus Infections (epidemiology), Female (MeSH), Hospitalization (MeSH), Humans (MeSH), Male (MeSH), Middle Aged (MeSH), Neural Networks, Computer (MeSH), Pandemics (MeSH), Pneumonia, Viral (diagnosis), Pneumonia, Viral (epidemiology), Prognosis (MeSH), ROC Curve (MeSH), United Kingdom (MeSH).
- MESH :
- geographic : United Kingdom.
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- Aged, Aged, 80 and over, Artificial Intelligence, Betacoronavirus, Comorbidity, Female, Hospitalization, Humans, Male, Middle Aged, Neural Networks, Computer, Pandemics, Prognosis, ROC Curve.
Abstract
BACKGROUND
The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2.
OBJECTIVE
We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN).
METHODS
We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2.
RESULTS
Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%.
CONCLUSIONS
This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems.
DOI: 10.2196/20259
PubMed: 32735549
PubMed Central: PMC7451108
Links to Exploration step
pubmed:32735549Le document en format XML
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Imperial College London, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Denny, Sarah" sort="Denny, Sarah" uniqKey="Denny S" first="Sarah" last="Denny">Sarah Denny</name><affiliation><nlm:affiliation>Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Mughal, Nabeela" sort="Mughal, Nabeela" uniqKey="Mughal N" first="Nabeela" last="Mughal">Nabeela Mughal</name><affiliation><nlm:affiliation>Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Moore, Luke" sort="Moore, Luke" uniqKey="Moore L" first="Luke" last="Moore">Luke Moore</name><affiliation><nlm:affiliation>Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32735549</idno><idno type="pmid">32735549</idno><idno type="doi">10.2196/20259</idno><idno type="pmc">PMC7451108</idno><idno type="wicri:Area/Main/Corpus">000893</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000893</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation.</title><author><name sortKey="Abdulaal, Ahmed" sort="Abdulaal, Ahmed" uniqKey="Abdulaal A" first="Ahmed" last="Abdulaal">Ahmed Abdulaal</name><affiliation><nlm:affiliation>Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Patel, Aatish" sort="Patel, Aatish" uniqKey="Patel A" first="Aatish" last="Patel">Aatish Patel</name><affiliation><nlm:affiliation>Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Charani, Esmita" sort="Charani, Esmita" uniqKey="Charani E" first="Esmita" last="Charani">Esmita Charani</name><affiliation><nlm:affiliation>NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Denny, Sarah" sort="Denny, Sarah" uniqKey="Denny S" first="Sarah" last="Denny">Sarah Denny</name><affiliation><nlm:affiliation>Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Mughal, Nabeela" sort="Mughal, Nabeela" uniqKey="Mughal N" first="Nabeela" last="Mughal">Nabeela Mughal</name><affiliation><nlm:affiliation>Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Moore, Luke" sort="Moore, Luke" uniqKey="Moore L" first="Luke" last="Moore">Luke Moore</name><affiliation><nlm:affiliation>Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Journal of medical Internet research</title><idno type="eISSN">1438-8871</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged (MeSH)</term><term>Aged, 80 and over (MeSH)</term><term>Artificial Intelligence (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Comorbidity (MeSH)</term><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (epidemiology)</term><term>Female (MeSH)</term><term>Hospitalization (MeSH)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Neural Networks, Computer (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (diagnosis)</term><term>Pneumonia, Viral (epidemiology)</term><term>Prognosis (MeSH)</term><term>ROC Curve (MeSH)</term><term>United Kingdom (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>United Kingdom</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Aged, 80 and over</term><term>Artificial Intelligence</term><term>Betacoronavirus</term><term>Comorbidity</term><term>Female</term><term>Hospitalization</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Neural Networks, Computer</term><term>Pandemics</term><term>Prognosis</term><term>ROC Curve</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2.</p></div><div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b></p><p>We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN).</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">32735549</PMID><DateCompleted><Year>2020</Year><Month>09</Month><Day>04</Day></DateCompleted><DateRevised><Year>2020</Year><Month>09</Month><Day>04</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1438-8871</ISSN><JournalIssue CitedMedium="Internet"><Volume>22</Volume><Issue>8</Issue><PubDate><Year>2020</Year><Month>08</Month><Day>25</Day></PubDate></JournalIssue><Title>Journal of medical Internet research</Title><ISOAbbreviation>J Med Internet Res</ISOAbbreviation></Journal><ArticleTitle>Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation.</ArticleTitle><Pagination><MedlinePgn>e20259</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.2196/20259</ELocationID><Abstract><AbstractText Label="BACKGROUND">The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2.</AbstractText><AbstractText Label="OBJECTIVE">We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN).</AbstractText><AbstractText Label="METHODS">We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2.</AbstractText><AbstractText Label="RESULTS">Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%.</AbstractText><AbstractText Label="CONCLUSIONS">This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems.</AbstractText><CopyrightInformation>©Ahmed Abdulaal, Aatish Patel, Esmita Charani, Sarah Denny, Nabeela Mughal, Luke Moore. 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