Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation.
Identifieur interne : 000319 ( Main/Exploration ); précédent : 000318; suivant : 000320Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation.
Auteurs : Ahmed Abdulaal [Royaume-Uni] ; Aatish Patel [Royaume-Uni] ; Esmita Charani [Royaume-Uni] ; Sarah Denny [Royaume-Uni] ; Nabeela Mughal [Royaume-Uni] ; Luke Moore [Royaume-Uni]Source :
- Journal of medical Internet research [ 1438-8871 ] ; 2020.
Descripteurs français
- KwdFr :
- Adulte d'âge moyen (MeSH), Betacoronavirus (MeSH), Comorbidité (MeSH), Courbe ROC (MeSH), Femelle (MeSH), Hospitalisation (MeSH), Humains (MeSH), Infections à coronavirus (diagnostic), Infections à coronavirus (épidémiologie), Intelligence artificielle (MeSH), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (diagnostic), Pneumopathie virale (épidémiologie), Pronostic (MeSH), Royaume-Uni (MeSH), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH).
- MESH :
- diagnostic : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Adulte d'âge moyen, Betacoronavirus, Comorbidité, Courbe ROC, Femelle, Hospitalisation, Humains, Intelligence artificielle, Mâle, Pandémies, Pronostic, Royaume-Uni, Sujet âgé, Sujet âgé de 80 ans ou plus.
- Wicri :
- geographic : Royaume-Uni.
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
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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<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>
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<term>Pneumonia, Viral (epidemiology)</term>
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<term>Betacoronavirus (MeSH)</term>
<term>Comorbidité (MeSH)</term>
<term>Courbe ROC (MeSH)</term>
<term>Femelle (MeSH)</term>
<term>Hospitalisation (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Infections à coronavirus (diagnostic)</term>
<term>Infections à coronavirus (épidémiologie)</term>
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<term>Pneumopathie virale (épidémiologie)</term>
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<term>Pneumopathie virale</term>
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<term>Pneumopathie virale</term>
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<term>Aged, 80 and over</term>
<term>Artificial Intelligence</term>
<term>Betacoronavirus</term>
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<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>
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<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. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.08.2020.</CopyrightInformation>
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<AffiliationInfo><Affiliation>NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom.</Affiliation>
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