Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.
Davide FerrariJovana MilicRoberto TonelliFrancesco GhinelliMarianna MeschiariSara VolpiMatteo FaltoniGiacomo FranceschiVittorio IadiserniaDina YaacoubGiacomo CiusaErica BaccaCarlotta RogatiMarco TutoneGiulia BurasteroAlessandro RaimondiMarianna MenozziErica FranceschiniGianluca CuomoLuca CorradiGabriella OrlandoAntonella SantoroMargherita DigaetanoCinzia PuzzolanteFederica CarliVanni BorghiAndrea BediniRiccardo FantiniLuca TabbìIvana CastaniereStefano BusaniEnrico Maria CliniMassimo GirardisMario SartiAndrea CossarizzaCristina MussiniFederica MandreoliPaolo MissierGiovanni GuaraldiPublished in: PloS one (2020)
This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.
Keyphrases
- respiratory failure
- global health
- machine learning
- public health
- decision making
- healthcare
- extracorporeal membrane oxygenation
- palliative care
- mechanical ventilation
- big data
- emergency department
- deep learning
- artificial intelligence
- quality improvement
- acute respiratory distress syndrome
- community acquired pneumonia