MEDAL: Measuring of emergency departments' adaptive load

Studies in Health Technology and Informatics

Volumn 160, Issue PART 1, 2010, Pages 218-222

  Vitkin, Edward ^a,b   Carmeli, Boaz ^a   Greenshpan, Ohad ^a   Baras, Dorit ^a   Marmor, Yariv ^b  

^a UNIVERSITY OF HAIFA   (Israel)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Emergency Department; Machine learning; Neural networks (computer); Operations research; Workload

Indexed keywords

DECISION MAKING; LEARNING SYSTEMS; NEURAL NETWORKS; OPERATIONS RESEARCH;

EMERGENCY DEPARTMENTS; FLEXIBLE FRAMEWORK; INNOVATIVE APPROACHES; MEASURING REAL TIME; MEDICAL INFORMATICS; OPERATIONAL INDICATOR; OPERATIONAL LOADS; WORKLOAD;

EMERGENCY ROOMS;

EID: 78649503043     PISSN: 09269630     EISSN: 18798365     Source Type: Conference Proceeding    
DOI: 10.3233/978-1-60750-588-4-218     Document Type: Conference Paper

References (10)

1. Sinreich D, and Marmor YN. 2005. Emergency department operations: the basis for developing a simulation tool. IIE Transactions 37:233-245.
2. Hall RW. Patient Flow: Reducing Delay in Healthcare Delivery. Springer. 2006
3. Green LV. Using Operations Research to reduce delays for healthcare. In Tutorials in Operations Research, eds. Zhi-Long Chen and S. Raghavan, 2008, 1-16. Hanover, MD: INFORMS.
4. Derlet RW, and Richards JR. Overcrowding in the nation's emergency departments: complex causes and disturbing effects. Annals of Emergency Medicine 2000 35:63-68.
5. Haykin S. Neural Networks: A Comprehensive Foundation, 1999 2nd edn, Upper Saddle River, NJ Prentice Hall, Inc.
6. Solberg LI, Asplin BR, Weinick RM, Magid DJ. Emergency department crowding: consensus development of potential measures. Ann Emerg Med. 2003 Dec; 42(6):824-34.
7. Khan J et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 2001, 7, 673-679.
8. Hinton GE and Salakhutdinov RR. Reducing the Dimensionality of Data with Neural Networks. Science 2006, 313(5786), 504.
9. Hornik K, Stinchcombe M, and White H. Multilayer feedforward networks are universal approximators. Neural Netw. 2, 5 (Jul. 1989), 359-366.
10. Wasserkrug S, Greenshpan O, Marmor YN, Carmeli B, Vortman P, Basis F, Schwartz D, and Mandelbaum A. InEDvance: Advanced IT in Support of Emergency Department Management. in NGITS 2009.