How artificial intelligence could transform emergency department operations - 19/07/18
Doi : 10.1016/j.ajem.2018.01.017
Yosef Berlyand, BA a, Ali S. Raja, MD, MBA, MPH a, b, c, Stephen C. Dorner, MD, MPH, MSc a, b, c, d, Anand M. Prabhakar, MD, MBA a, c, e, Jonathan D. Sonis, MD a, b, c, Ravi V. Gottumukkala, MD a, c, e, Marc David Succi, MD a, c, e, f, Brian J. Yun, MD, MBA, MPH a, b, c, ⁎ 
a Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States
b Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
c Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
d Harvard Affiliated Emergency Medicine Residency Program, 5 Emerson Place, Suite 101, Boston, MA 02114, United States
e Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
f Medically Engineered Solutions in Healthcare (MESH) Incubator, Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
⁎Corresponding author at: Department of Emergency Medicine, Massachusetts General Hospital, 0 Emerson Place, Suite 3B, Boston, MA 02114, United States.Department of Emergency MedicineMassachusetts General Hospital0 Emerson Place, Suite 3BBostonMA02114United States
Keywords : Artificial intelligence, Machine learning, Emergency radiology, Emergency medicine, Emergency department operations, Triage, Natural language processing
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© 2018
Elsevier Inc. Tutti i diritti riservati.
Vol 36 - N° 8
P. 1515-1517 - agosto 2018 Ritorno al numero
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