A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

A model to integrate Data Mining and On-line Analytical Processing: with application to Real Time Process Control

Since the widespread use of computers in business and industry, a lot of research has been done on the design of computer systems to support the decision making task. Decision support systems support decision makers in solving unstructured decision problems by providing tools to help understand and analyze decision problems to help make better decisions. Artificial intelligence is concerned with creating computer systems that perform tasks that would require intelligence if performed by humans. Much research has focused on using artificial intelligence to develop decision support systems to provide intelligent decision support. Knowledge discovery from databases, centers around data mining algorithms to discover novel and potentially useful information contained in the large volumes of data that is ubiquitous in contemporary business organizations. Data mining deals with large volumes of data and tries to develop multiple views that the decision maker can use to study this multi-dimensional data. On-line analytical processing (OLAP) provides a mechanism that supports multiple views of multi-dimensional data to facilitate efficient analysis. These two techniques together can provide a powerful mechanism for the analysis of large quantities of data to aid the task of making decisions. This research develops a model for the real time process control of a large manufacturing process using an integrated approach of data mining and on-line analytical processing. Data mining is used to develop models of the process based on the large volumes of the process data. The purpose is to provide prediction and explanatory capability based on the models of the data and to allow for efficient generation of multiple views of the data so as to support analysis on multiple levels. Artificial neural networks provide a mechanism for predicting the behavior of nonlinear systems, while decision trees provide a mechanism for the explanation of states of systems given a set of inputs and outputs. OLAP is used to generate multidimensional views of the data and support analysis based on models developed by data mining. The architecture and implementation of the model for real-time process control based on the integration of data mining and OLAP is presented in detail. The model is validated by comparing results obtained from the integrated system, OLAP-only and expert opinion. The system is validated using actual process data and the results of this verification are presented. A discussion of the results of the validation of the integrated system and some limitations of this research with discussion on possible future research directions is provided

A New Approach for Determination of Tunnel Supporting System Using Analytical Hierarchy Process (AHP)

In underground mining, the selection of support system for mine tunnel development plays a significant role in safety and economics of operations. Traditionally, such selection is on the basis of the experience of the design engineer. Nevertheless, the validity of such selection is questionable. A new approach for selecting the optimum tunnel support system based on Analytical Hierarchy Process (AHP) is proposed. In this new approach, the selection of the tunnel support system is considered as a multi criteria decision-making problem. Firstly, by using the numerical Finite Difference Method (FDM), based on technical and stability parameters of the tunnel, different support systems are specified. Then, by considering the economics and performance indices of each support system, a decision tree based on AHP model is generated and the optimum support system is selected. As a field study, the method is applied to Tabas collieries in Iran. It is concluded that the proposed support system determination is advantageous compared to other alternatives. Therefore, the proposed approach can assist the engineer in selection of optimum tunnel support system in different underground mining situations

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated

Workflow simulation for operational decision support using YAWL and ProM

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of work ow management. To do this we exploit not only the work ow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the work ow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for dierent scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM. This technical report contains a detailed description of how a simulation model including operational decision support can be generated by our software based on the running example

Discovering behavioral patterns among air pollutants: A data mining approach

Air pollutants affect both human health and the environment. For this reason, environmental managers and urban planners focus their efforts in monitoring air pollution. In this context, complete information is required to support the decision-making process to improve the quality of life in urban zones. Hence, it is important to extract knowledge not only on concentration levels but associations between air pollutants. Based on the Cross-industry standard process for data mining, this paper presents an approach which leads to identify correlations and incidence between the most harmful pollutants in the Andean Region: Ozone, Carbon monoxide, Sulfur dioxide, Nitrogen dioxide and, Particulate material. This paper describes an experiment using a real dataset from a monitoring station in Cuenca, Ecuador located in the Andean region.  The results show that the proposed approach is effective to extract knowledge useful to support the evaluation of air quality in urban zones. In addition, this approach provides a starting point for future data mining applications for the analysis of air pollution in the context of the Andean region

A Data Centric Privacy Preserved Mining Model for Business Intelligence Applications

In present day competitive scenario, the techniques such as data warehouse and on-line analytical process (OLAP) have become a very significant approach for decision support in data centric applications and industries. In fact the decision support mechanism puts certain moderately varied needs on database technology as compared to OLAP based applications. Data centric decision support schemes (DSS) like data warehouse might play a significant role in extracting details from various areas and for standardizing data throughout the organization to achieve a singular way of detail presentation. Such efficient data presentation facilitates information for decision making in business intelligence (BI) applications in various industrial services. In order to enhance the effectiveness and robust computation in BI applications, the optimization in data mining and its processing is must. On the other hand, being in a multiuser scenario, the security of data on warehouse is also a critical issue, which is not explored till date. In this paper a data centric and service oriented privacy preserved model for BI applications has been proposed. The optimization in data mining has been accomplished by means of C5.0 classification algorithm and comparative study has been done with C4.5 algorithm. The implementation of enhanced C5.0 algorithm with BI model would provide much better performance with privacy preservation facility for Business Intelligence applications

Data Mining as Support to Knowledge Management in Marketing

Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers\u27 profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability

The Impact of Local Participation on Community Support for Natural Resource Management: The Case of Mining in Northern Canada and Northern Sweden

Due to its oftentimes complex, contested, and multi-scale character, natural resource management (NRM) tends to be a challenging task that has been met with various political approaches in order to meet demands for legitimacy. One approach to enhancing the legitimacy of NRM that has gained increased attention within the academic literature is the adoption of local participatory democracy in decision-making processes. Advocates of participatory democracy in NRM propose that local participation achieves the following outcomes: increased legitimacy because it ensures that local needs and priorities are successfully met; decision-making based on more complete information, which helps avoid unexpected negative outcomes; and a sense of belonging and influence among the public, leading to increased perceptions of support and partnership, as opposed to NRM which is imposed on the community. Nevertheless, comprehensive empirical studies that document how public participation affects legitimacy remain rare. Using 2015 data collected on people’s attitudes towards mining in northern Saskatchewan, Canada, and Norrbotten and Västerbotten counties, Sweden, this paper empirically assesses whether and how perceptions of local participation affect the legitimacy of mining development. In turn, this paper finds that perceived public participation does affect the public’s propensity to support mining development and this propensity is mediated by people’s perceptions of the interests present in the decision-making process, their normative beliefs concerning which actors should be allowed to participate in the decision-making process, and certain individual-level and contextual-level factors