As an aid to predicting future hospital admissions, we compare use of the Multinomial Logit and the Utility Maximising Nested Logit models to describe how patients choose their hospitals. The models are fitted to real data from Derbyshire, United Kingdom, which lists the postcodes of more than 200,000 admissions to six different local hospitals. Both elective and emergency admissions are analysed for this mixed urban/rural area. For characteristics that may affect a patient's choice of hospital, we consider the distance of the patient from the hospital, the number of beds at the hospital and the number of car parking spaces available at the hospital, as well as several statistics publicly available on National Health Service (NHS) websites: an average waiting time, the patient survey score for ward cleanliness, the patient safety score and the inpatient survey score for overall care. The Multinomial Logit model is successfully fitted to the data. Results obtained with the Utility Maximising Nested Logit model show that nesting according to city or town may be invalid for these data; in other words, the choice of hospital does not appear to be preceded by choice of city. In all of the analysis carried out, distance appears to be one of the main influences on a patient's choice of hospital rather than statistics available on the Internet.
Decision making on facility locations for blood services has an impact on the efficiency of supply chain and logistics systems. In the blood supply chain operated by the Thai Red Cross Society (TRCS), problems are faced with amounts of blood collected in different provinces of Thailand being insufficient to meet demand. At the present time, TRCS operates one National Blood Centre in the capital and twelve Regional Blood Centres in different provinces to collect, prepare, test, and distribute safe blood. A proposal has been made to extend this network of blood centres using low-cost collection and distribution centres. Increasing numbers of fixed collection sites can improve access for donors. In addition, some facilities will be able to perform preparation and storage for blood that hospitals can receive directly. This paper addresses the selection of sites for two types of facility, either a blood donation room only or donation room with a distribution centre. A range of investment budgets is investigated to inform the strategic plan of this non-profit organisation. We present a novel binary integer programming model for this location-allocation problem based on objectives of improving the supply of blood products while reducing costs of transportation. Computational results are reported, using real life data, that are of practical importance to decision makers.
This research objective is to study the latent topics analysis in selling post of real estate of second-hand condominium by using Latent Dirichlet Allocation (LDA) and build a price prediction model of second-hand condominium using multiple linear regression and artificial neural networks by measuring and comparing the performance of the second hand condominium price prediction model with root mean square error (RMSE). This experiment included four variables are room size, number of bathroom, number of bedroom and latent topics from LDA. The result of LDA indicated that selling post of real estate can be separated into 4 topics, in which finding the factors that affect the price use the regression analysis method to get five variables are room size, number of bathroom, floors, topic 2 and topic 4. The RMSE based on the multiple linear regression analysis was 1.349, while the RMSE based on artificial neural network was 1.156. Thus, it can be concluded that the predictive model using the artificial neural networks is superior to multiple linear regression.
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