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The age again in the eye of the COVID-19 storm: evidence-based decision making.

María C MartínAurora Jurado RogerCristina Abad-MolinaAntonio OrduñaOscar YarceAna M NavasVanesa CunillDanilo EscobarFrancisco BoixSergio Burillo-SanzMaría C Vegas-SánchezYesenia Jiménez-de Las PozasJosefa MeleroMarta AguilarOana Irina SobieschiMarcos López-HoyosGonzalo Ocejo-VinyalsDavid San SegundoDelia AlmeidaSilvia MedinaLuis FernándezEsther VergaraBibiana QuirantEva Martínez-CáceresMarc BoigesMarta AlonsoLaura Esparcia-PinedoCelia López-SanzJavier Muñoz-VicoSerafín López-PalmeroAntonio TrujilloPaula ÁlvarezÁlvaro PradaDavid MonzónJesús OntañónFrancisco M MarcoSergio MoraRicardo RojoGema González-MartínezMaría T Martínez-SaavedraJuana Gil-HerreraSergi Cantenys-MolinaManuel HernándezJanire Perurena-PrietoBeatriz Rodríguez-BayonaAlba MartínezEsther OcañaJuan Molina
Published in: Immunity & ageing : I & A (2021)
Age and sex together with selected laboratory parameters on admission can help us predict COVID-19 severity and, therefore, make clinical and resource management decisions. Demographic features associated with lockdown might affect the homogeneity of the data and the robustness of the results.
Keyphrases
  • coronavirus disease
  • sars cov
  • decision making
  • emergency department
  • respiratory syndrome coronavirus
  • big data
  • resting state
  • machine learning
  • artificial intelligence
  • data analysis