@article{(Open Science Index):https://publications.waset.org/pdf/15141,
	  title     = {A Novel Approach to Handle Uncertainty in Health System Variables for Hospital Admissions},
	  author    = {Manisha Rathi and  Thierry Chaussalet},
	  country	= {},
	  institution	= {},
	  abstract     = {Hospital staff and managers are under pressure and
concerned for effective use and management of scarce resources. The
hospital admissions require many decisions that have complex and
uncertain consequences for hospital resource utilization and patient
flow. It is challenging to predict risk of admissions and length of stay
of a patient due to their vague nature. There is no method to capture
the vague definition of admission of a patient. Also, current methods
and tools used to predict patients at risk of admission fail to deal with
uncertainty in unplanned admission, LOS, patients- characteristics.
The main objective of this paper is to deal with uncertainty in
health system variables, and handles uncertain relationship among
variables. An introduction of machine learning techniques along with
statistical methods like Regression methods can be a proposed
solution approach to handle uncertainty in health system variables. A
model that adapts fuzzy methods to handle uncertain data and
uncertain relationships can be an efficient solution to capture the
vague definition of admission of a patient.},
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {6},
	  number    = {10},
	  year      = {2012},
	  pages     = {2576 - 2579},
	  ee        = {https://publications.waset.org/pdf/15141},
	  url   	= {https://publications.waset.org/vol/70},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 70, 2012},
	}