Integrating Spatial-Temporal Risk Factors for an Ambulance Allocation Strategy: A Case Study in Bangkok

Dedicated emergency medical services (EMS) are important to patients’ chances of survival. In particular, the quicker such services arrive at the scene of an incident, the higher the survival rate. Therefore, the management of ambulance bases is an essential aspect of emergency medical services. Further, the locations of ambulance bases are determined based on patient demand. However, in practice, many elements should be taken into account in a risk assessment of given areas within a locale. Specifically, each area should be assessed for the number and severity of accidents that ordinarily take place there, the number and size of the public events it hosts, its population density, and the number of elderly people resident. In this study, we use a spatial-temporal approach to integrate those factors into a risk assessment of areas relative to each other in a locale. Based on this risk assessment, we determine the optimal locations for ambulance bases in order to minimize response time. We validate our approach using Bangkok as a case study

Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset

A model-building framework is proposed that combines two data mining techniques, TreeNet and association rules analysis (ASA) with multinomial logit model building. TreeNet provides plots that play a key role in transforming quantitative variables into better forms for the model fit, whereas ASA is important in finding interactions (low- and high-order) among variables. With the implementation of TreeNet and ASA, new variables and interactions are generated, which serve as candidate predictors in building an optimal multinomial logit model. A real-life example in the context of health care is used to illustrate the major role of these newly generated variables and interactions in advancing multinomial logit modeling to a new level of performance. This method has an explanatory and predictive ability that cannot be achieved using existing methods

Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets

In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve Bayes classifier—were combined to improve the performance of the latter. A classification tree was used to discretize quantitative predictors into categories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas

Alcohol Consumption in Thailand: A Study of the Associations between Alcohol, Tobacco, Gambling, and Demographic Factors

This paper provides a thorough study of alcohol consumption in Thailand in terms of the relationships between this activity and tobacco consumption, gambling consumption, and demographic factors. Three statistical models and data-mining techniques—logistic regression, Treenet, and directed acyclic graphs—are used to analyze datasets drawn from socioeconomic surveys of 43,844 Thai households conducted in 2009. From logistic regression, we find that the region where the household is located, urban/rural location of the household, household income, tobacco household expenditure, gambling household expenditure, education, religion, marital status, gender, age, and work status of the household head are all associated with the alcohol consumption of households. The strongest predictors of household alcohol consumption are tobacco expenditure, religion and sex of the household head. From Treenet, we find that the proportion of tobacco expenditure is the most important factor in explaining the proportion of alcohol expenditure. From the directed acyclic graph (DAG), we find that the proportion of alcohol expenditure has a direct effect on both the proportion of tobacco expenditure and the proportion of gambling expenditure. We expect our results to be useful to researchers and government practitioners in their efforts to design and implement programs targeting households that include alcohol-dependent members and to thereby reduce alcohol consumption in Thailand

Integrating Spatial-Temporal Risk Factors for an Ambulance Allocation Strategy: A Case Study in Bangkok

Dedicated emergency medical services (EMS) are important to patients’ chances of survival. In particular, the quicker such services arrive at the scene of an incident, the higher the survival rate. Therefore, the management of ambulance bases is an essential aspect of emergency medical services. Further, the locations of ambulance bases are determined based on patient demand. However, in practice, many elements should be taken into account in a risk assessment of given areas within a locale. Specifically, each area should be assessed for the number and severity of accidents that ordinarily take place there, the number and size of the public events it hosts, its population density, and the number of elderly people resident. In this study, we use a spatial-temporal approach to integrate those factors into a risk assessment of areas relative to each other in a locale. Based on this risk assessment, we determine the optimal locations for ambulance bases in order to minimize response time. We validate our approach using Bangkok as a case study