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Paper Details

Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing.
PLoS One
17
2020
Patients, cardiopulmonary arrest, patient, patients
Adult, Cohort Studies, Emergency Service, Hospital, Female, Forecasting, Heart Arrest, Hospitalization, Humans, Logistic Models, Machine Learning, Male, Middle Aged, Natural Language Processing, Patient Acuity, Portugal, ROC Curve, Risk Assessment, Risk Factors, Triage
Author NameAffiliation
Marta FernandesInstituto Superior Tecnico, Universidade de Lisboa
R??ben MendesInstituto Superior Tecnico, Universidade de Lisboa
Susana M VieiraInstituto Superior Tecnico, Universidade de Lisboa
Francisca LeiteHospital da Luz Learning Health
Carlos PalosHospital Beatriz Angelo
Alistair E W JohnsonMassachusetts Institute of Technology
Stan N FinkelsteinInstitute for Data, Massachusetts Institute of Technology
Steven HorngDepartment of Emergency Medicine / Division of Clinical Informatics / Center for Healthcare Delivery Science, Beth Israel Deaconess Medical Center
Leo Anthony CeliMassachusetts Institute of Technology
Leo Anthony CeliBeth Israel Deaconess Medical Center
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