Data Mining in Health-Care: Issues and a Research Agenda

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

Simulation of deposit parameters in underground development mining

The article is aimed at improving development mining to prepare an ore body for stoping by access ramps to provide comfortable conditions and high technical and economic indices in underground mining. Efficient parameters of underground mining are chosen in the course of simulating data on the mining theory and practice considering ore losses and dilution on the basis of critical analysis of uranium mining enterprises’ activities. The research provides data on geological and engineering zoning of an ore deposit and physical-mechanical properties of ore bearing rocks. The advanced experience is systemized and there is provided system analysis of modern development mining schemes with access ramps (ring, spiral, one-way inclined, central inclined and across the strike). The research recommends schemes of development mining and substantiates their advantages. There are quantitative indices of physical simulation of development variants as to drawn ore quality according to criteria of soil location in ore draw points. The scientific novelty implies developing the criterion of optimality and ranking variants of development mining according to technical-economic and geomechanical indices considering some technological factors as well as the number of stopes operating simultaneously on the level. The study consists in increasing authenticity of development projects through applying complex schemes of access ramps according to the complex criterion of increasing mining depths, equipment application, ventilation and underground mine capacity

Managing Social Responsibilities in the Extractive Industries: Exploring Cultural Impacts of Displacement and Resettlement Practice in Mining

This study aims to develop better insights into displacement and resettlement practice in mining and underlying impacts on a community’s culture, in light of the growing institutionalisation of the practice in such settings. We address the question whether displacement and resettlement practice in mining is, in fact, a realistic ambition to restore and prioritise a displaced community’s cultural values or rather a socially (or ethically) responsible approach to mining operations. This study rests on a qualitative, in-depth case study analysis. The case involves two communities affected by displacement and resettlement practice in mining, namely, Akoti in the Sefwi Wiawso Municipality and Obrayeko in the Bibiani Anhwiaso Bekwai Municipality; both located in the Western North region of Ghana. Data were collected through interviews with four key informants throughout an ongoing dialogue with them over a period of six months. Additionally, informal talks with various stakeholders of the two communities were performed. A qualitative narrative analysis method was used to analyse the data, and thereby, avoiding data fragmentation. The findings suggest that displacement and resettlement practice in mining face some overarching issues, including decreasing social trust, extent of ethical CSR practice, and challenges associated with loss of ancestral lands/heritage. This study’s findings serve to inform corporate decisions as to the internal awareness of culture and associated elements, i.e., customs, traditions, norms, beliefs and value systems that impinge on displacement and resettlement practice in mining. Limitations of this study include limited data available, particularly interviews, which provides basis for future research. Our findings contribute to the literature by identifying the culture-related issues that arise from displacement and resettlement practice in mining, and responds to calls for further research that takes a sector-specific approach – both in mining and in other sectors where resettlement is common, and also calls for further research that explores culture-related issues at stake in corporate activities. Keywords:Social responsibilities, Mining, Displacement and resettlement practice, Developing countries, Practice theory, Business ethics DOI: 10.7176/JRDM/92-04 Publication date:October 31st 202

Relational Algebra for In-Database Process Mining

The execution logs that are used for process mining in practice are often obtained by querying an operational database and storing the result in a flat file. Consequently, the data processing power of the database system cannot be used anymore for this information, leading to constrained flexibility in the definition of mining patterns and limited execution performance in mining large logs. Enabling process mining directly on a database - instead of via intermediate storage in a flat file - therefore provides additional flexibility and efficiency. To help facilitate this ideal of in-database process mining, this paper formally defines a database operator that extracts the 'directly follows' relation from an operational database. This operator can both be used to do in-database process mining and to flexibly evaluate process mining related queries, such as: "which employee most frequently changes the 'amount' attribute of a case from one task to the next". We define the operator using the well-known relational algebra that forms the formal underpinning of relational databases. We formally prove equivalence properties of the operator that are useful for query optimization and present time-complexity properties of the operator. By doing so this paper formally defines the necessary relational algebraic elements of a 'directly follows' operator, which are required for implementation of such an operator in a DBMS

Data-Driven Application Maintenance: Views from the Trenches

In this paper we present our experience during design, development, and pilot deployments of a data-driven machine learning based application maintenance solution. We implemented a proof of concept to address a spectrum of interrelated problems encountered in application maintenance projects including duplicate incident ticket identification, assignee recommendation, theme mining, and mapping of incidents to business processes. In the context of IT services, these problems are frequently encountered, yet there is a gap in bringing automation and optimization. Despite long-standing research around mining and analysis of software repositories, such research outputs are not adopted well in practice due to the constraints these solutions impose on the users. We discuss need for designing pragmatic solutions with low barriers to adoption and addressing right level of complexity of problems with respect to underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th International Workshop on Software Engineering Research and Industrial Practice (SER&IP), IEEE Press, pp. 48-54, 201

Data mining in medical records for the enhancement of strategic decisions: a case study

The impact and popularity of competition concept has been increasing in the last decades and this concept has escalated the importance of giving right decision for organizations. Decision makers have encountered the fact of using proper scientific methods instead of using intuitive and emotional choices in decision making process. In this context, many decision support models and relevant systems are still being developed in order to assist the strategic management mechanisms. There is also a critical need for automated approaches for effective and efficient utilization of massive amount of data to support corporate and individuals in strategic planning and decision-making. Data mining techniques have been used to uncover hidden patterns and relations, to summarize the data in novel ways that are both understandable and useful to the executives and also to predict future trends and behaviors in business. There has been a large body of research and practice focusing on different data mining techniques and methodologies. In this study, a large volume of record set extracted from an outpatient clinic’s medical database is used to apply data mining techniques. In the first phase of the study, the raw data in the record set are collected, preprocessed, cleaned up and eventually transformed into a suitable format for data mining. In the second phase, some of the association rule algorithms are applied to the data set in order to uncover rules for quantifying the relationship between some of the attributes in the medical records. The results are observed and comparative analysis of the observed results among different association algorithms is made. The results showed us that some critical and reasonable relations exist in the outpatient clinic operations of the hospital which could aid the hospital management to change and improve their managerial strategies regarding the quality of services given to outpatients.Decision Making, Medical Records, Data Mining, Association Rules, Outpatient Clinic.