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Big data and machine learning in critical care: Opportunities for collaborative research
ArticleAbstract: The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the posPalabras claves:Artificial Intelligence, BIG DATA, Clinical databases, collaborative work, Datathon, Machine learning, MIMIC IIIAutores:Alonso J.P., Benítez J.C.M., Bodí M., Cabrera M.F., Cabrera M.N., Celi L.A., Chafer J., de la Hoz M.Á.A., Deliberato R., Ferrer A.G., Ficο G., Gómez J., González M.Á., Hernanzez L., Lombroni I., López L., Luengo H.G., Malpica A.B., María Teresa Arredondo, Mayol J., Merino B., Paik K., Perdomo J.M.G., Pollard T., Raffa J., Ramírez Á.D.P., Reiz A.N., Rey Á., Rodríguez A., Sagasti F.M., Sánchez García M., Torres F.Fuentes:scopusIn vitro evaluation of aerosol delivery of aztreonam lysine (AZLI): an adult mechanical ventilation model
ArticleAbstract: Background: The delivery profile of Aztreonam lysine (AZLI) during mechanical ventilation (MV) is unPalabras claves:aerosol delivery, Aztreonam lysine, In vitro model, Mechanical Ventilation, nebulized antibioticAutores:Bodí M., Cabrera M., Canadell L., Diego Barahona, Ehrmann S., Martín-Loeches I., Restrepo M.I., Reyes L.F., Rodríguez A., Trefler S., Vecellio L.Fuentes:scopusRelationship between acute kidney injury and serum procalcitonin (PCT) concentration in critically ill patients with influenza infection
ArticleAbstract: Introduction: Serum procalcitonin (PCT) concentration could be increased in patients with renal dysfPalabras claves:Creatinine, influenza, Procalcitonin, Renal dysfunction, ureaAutores:Bodí M., Díaz E., Diego Barahona, Guardiola J., Martín-Loeches I., Monclou J., Restrepo M.I., Reyes L.F., Rodríguez A., Sirgo G., Solé-Violán J., Suberviola B.Fuentes:scopus