A Discrete Conditional Phase-Type Model Utilising a Survival Tree for the Identification of Elderly Patient Cohorts and Their Subsequent Prediction of Length of Stay in Hospital

2016 ◽  
Author(s):  
Andrew S. Gordon ◽  
Adele H. Marshall ◽  
Mariangela Zenga
Keyword(s):  
Elderly Patient ◽  
Length Of Stay ◽  
Type Model ◽  
Phase Type ◽  
Conditional Phase ◽  
Survival Tree

Phase Type Viewpoint Spectrum Sensing for Cognitive Radios

Frequenz ◽  
2015 ◽  
Vol 69 (9-10) ◽  
Author(s):  
Mohammadreza Amini ◽  
Asra Mirzavandi
Keyword(s):  
Spectrum Sensing ◽  
Cognitive Radios ◽  
Type Model ◽  
Computationally Efficient ◽  
Channel State ◽  
Secondary Users ◽  
Trade Off ◽  
Phase Type ◽  
Error Probabilities ◽  
Sensing Time

AbstractSpectrum sensing is one of the main functionalities of cognitive radios to find transmission opportunities without interfering primary users’ transmission. The more accurate and efficient the spectrum sensing is, the higher the throughput of secondary and primary networks is achieved. This paper presents adaptive spectrum sensing method based on phase type modelling that is computationally efficient for secondary users to conclude about the channel state (idle or busy) under collision constraint. The parameters of phase type model can be adjusted based on the desired operating point of the receiver sensor in its ROC curve. The presented approach can run a simple trade off between sensing time and the two error probabilities of the receiver sensor i.e. False alarm and Miss-detection, the trade off that cannot be easily achieved in other sensing method.

Impact of an orthogeriatrician on length of stay of elderly patient with hip fracture

2020 ◽  
Vol 31 (11) ◽  
pp. 2161-2166
Author(s):  
C. Aletto ◽  
R. Aicale ◽  
G. Pezzuti ◽  
F. Bruno ◽  
N. Maffulli
Keyword(s):  
Elderly Patient ◽  
Hip Fracture ◽  
Length Of Stay

Trend Analysis of Length of Stay Data via Phase-Type Models

2011 ◽  
Vol 2 (3) ◽  
pp. 37-51 ◽  
Author(s):  
Truc Viet Le ◽  
Chee Keong Kwoh ◽  
Kheng Hock Lee ◽  
Eng Soon Teo
Keyword(s):  
Length Of Stay ◽  
Temporal Trends ◽  
Developed Countries ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Important Indicator ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Hospital Resources ◽  
The Common

The populations in many developed countries throughout the world are aging rapidly and the number of geriatric patients is expected to rise steeply in those countries. This will exert greater pressures on the management of hospital resources as a result. Hospital length of stay (LOS) is an important indicator of hospital activity and management because of its direct relation to resource consumption. Planning of hospital resources according to identified trends of LOS is, thus, an effective way to meet such future needs. In this paper, the authors propose a method to analyze the temporal trends of LOS based on the Coxian phase-type distributions, a special type of continuous-time Markov process. By fitting and regressing the probabilities of discharge from each phase of the distribution on time, the authors have found a growing trend in the proportion of long-staying patients in their sample of stroke patients from a general hospital in Singapore. The authors compare the yearly, quarterly and monthly trends over the same period to see the common pattern. The datasets were also robustified by bootstrapping to aid the analysis.

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