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Open Access

Peer-reviewed

Research Article

A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS) among COVID-19 patients

Nicola Lazzarini ,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft

* E-mail: nicola.lazzarini@iqvia.com

Affiliation Real World Analytics & AI, IQVIA, Cambridge, United Kingdom

https://orcid.org/0000-0003-1783-5488

⨯
Avgoustinos Filippoupolitis,

Roles Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliation Real World Analytics & AI, IQVIA, Cambridge, United Kingdom
⨯
Pedro Manzione,

Roles Data curation, Formal analysis, Methodology, Validation, Writing – review & editing

Affiliation Strategic Analytics & Insights, IQVIA, Saint-Prex, Switzerland

https://orcid.org/0000-0001-7985-1446

⨯
Hariklia Eleftherohorinou

Roles Conceptualization, Methodology, Writing – review & editing

Affiliation Innovation Ventures & Strategic Partnerships, IQVIA, Athens, Greece
⨯

A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS) among COVID-19 patients

Nicola Lazzarini,
Avgoustinos Filippoupolitis,
Pedro Manzione,
Hariklia Eleftherohorinou

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Published: July 28, 2022
https://doi.org/10.1371/journal.pone.0271227

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COVID 19
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Machine learning
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Acute respiratory distress syndrome
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Respiratory infections
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Machine learning algorithms
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Virus testing
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Decision tree learning
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Forecasting
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