Clean birth kits to improve birth practices: development and testing of a country level decision support tool

Background: Clean birth practices can prevent sepsis, one of the leading causes of both maternal and newborn mortality. Evidence suggests that clean birth kits (CBKs), as part of package that includes education, are associated with a reduction in newborn mortality, omphalitis, and puerperal sepsis. However, questions remain about how best to approach the introduction of CBKs in country. We set out to develop a practical decision support tool for programme managers of public health systems who are considering the potential role of CBKs in their strategy for care at birth. Methods: Development and testing of the decision support tool was a three-stage process involving an international expert group and country level testing. Stage 1, the development of the tool was undertaken by the Birth Kit Working Group and involved a review of the evidence, a consensus meeting, drafting of the proposed tool and expert review. In Stage 2 the tool was tested with users through interviews (9) and a focus group, with federal and provincial level decision makers in Pakistan. In Stage 3 the findings from the country level testing were reviewed by the expert group. Results: The decision support tool comprised three separate algorithms to guide the policy maker or programme manager through the specific steps required in making the country level decision about whether to use CBKs. The algorithms were supported by a series of questions (that could be administered by interview, focus group or questionnaire) to help the decision maker identify the information needed. The country level testing revealed that the decision support tool was easy to follow and helpful in making decisions about the potential role of CBKs. Minor modifications were made and the final algorithms are presented. Conclusion: Testing of the tool with users in Pakistan suggests that the tool facilitates discussion and aids decision making. However, testing in other countries is needed to determine whether these results can be replicated and to identify how the tool can be adapted to meet country specific needs

The design of a knowledge-based guidance system for an intelligent multiple objective decision support system (IMODSS)

This paper describes a project that extends the multiple objective decision support system (MODSS) by offering knowledge-based guidance to an intelligent multiple objective decision support system (IMODSS). This IMODSS integrates expert system (ES), multiple objective decision-making (MODM) methodologies, graphical user interface (GUI) and decision support systems (DSS) technologies. This IMODSS uses an expert system shell CLIPS to build a knowledge base to guide the decision-makers (DMs) to select the most suitable MODM method(s) from the MODM methodology base in order to solve their particular decision problems. This IMODSS has been implemented and tested. This paper mainly discusses the design and implementation of the knowledge-based intelligent guidance subsystem in IMODSS

Practical applications of data mining in plant monitoring and diagnostics

Using available expert knowledge in conjunction with a structured process of data mining, characteristics observed in captured condition monitoring data, representing characteristics of plant operation may be understood, explained and quantified. Knowledge and understanding of satisfactory and unsatisfactory plant condition can be gained and made explicit from the analysis of data observations and subsequently used to form the basis of condition assessment and diagnostic rules/models implemented in decision support systems supporting plant maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of in-service distribution circuit breakers and empirical UHF data captured from laboratory experiments simulating partial discharge defects typically found in HV transformers. This discovered knowledge then forms the basis of two separate decision support systems for the condition assessment/defect clasification of these respective plant items

Database of Best Practices and Decision Support Web-based System for Construction Innovation

Many construction and facilities management Web sites can be found on the Internet. The interested parties on construction and facilities management Web sites can find databases of best practices, calculators, analyzers, software, expert and decision support systems, neural networks, etc. Technological innovation mainly through changes in the availability of information and communication technology inclusive databases of best practices, calculators, analyzers, software, neural networks, decision support and expert systems that have been provided by a variety of new services developed by the construction and facilities management sectors. Most of all calculators, analyzers, software, decision support and expert systems, neural networks and on-line systems seek to find out how to make the most economic decisions and most of all these decisions are intended only for economic objectives. Alternatives under evaluation have to be evaluated not only from the economic position, but take into consideration qualitative, technical, technological and other characteristics as well. Based on the analysis of the existing calculators, analyzers, information, expert and decision support systems, neural networks and in order to determine most efficient versions of best practices a Decision Support Web-Based System for Construction Innovation (IDSS) was developed by Vilnius Gediminas Technical University