Research ArticleOriginal Research
Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach
David Castiñeira, Katherine R Schlosser, Alon Geva, Amir R Rahmani, Gaston Fiore, Brian K Walsh, Craig D Smallwood, John H Arnold and Mauricio Santillana
Respiratory Care September 2020, 65 (9) 1367-1377; DOI: https://doi.org/10.4187/respcare.07561
David Castiñeira
Massachusetts Institute of Technology, Cambridge, Massachusetts.
Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts.
Katherine R Schlosser
Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts
Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts.
Department of Pediatrics, Division of Pediatric Critical Care, Columbia University Irving Medical Center, New York, New York.
Alon Geva
Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts.
Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts
Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts.
Amir R Rahmani
Data Science Institute, Columbia University at the time the research was conducted.
Gaston Fiore
Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts.
Brian K Walsh
Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts
Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts.
Department of Allied Health Professions, School of Health Sciences, Liberty University, Lynchburg, Virginia.
Craig D Smallwood
Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts
Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts.
John H Arnold
Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts
Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts.
Mauricio Santillana
Department of Pediatrics, Harvard Medical School, Boston, Massachusetts.
In this issue
Respiratory Care
Vol. 65, Issue 9
1 Sep 2020
Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach
David Castiñeira, Katherine R Schlosser, Alon Geva, Amir R Rahmani, Gaston Fiore, Brian K Walsh, Craig D Smallwood, John H Arnold, Mauricio Santillana
Respiratory Care Sep 2020, 65 (9) 1367-1377; DOI: 10.4187/respcare.07561
Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach
David Castiñeira, Katherine R Schlosser, Alon Geva, Amir R Rahmani, Gaston Fiore, Brian K Walsh, Craig D Smallwood, John H Arnold, Mauricio Santillana
Respiratory Care Sep 2020, 65 (9) 1367-1377; DOI: 10.4187/respcare.07561