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Knowledge discovery in biomedicine is a time-consuming process starting from the basic research, through preclinical testing, towards possible clinical applications. Crossing of conceptual boundaries is often needed for groundbreaking biomedical research that generates highly inventive discoveries. We demonstrate the ability of a creative literature mining method to advance valuable new discoveries based on rare ideas from existing literature. When emerging ideas from scientific literature are put together as fragments of knowledge in a systematic way, they may lead to original, sometimes surprising, research findings. If enough scientific evidence is already published for the association of such findings, they can be considered as scientific hypotheses. In this chapter, we describe a method for the computer-aided generation of such hypotheses based on the existing scientific literature. Our literature-based discovery of NF-kappaB with its possible connections to autism was recently approved by scientific community, which confirms the ability of our literature mining methodology to accelerate future discoveries based on rare ideas from existing literature.

In the field of autism, an enormous increase in available information makes it very difficult to connect fragments of knowledge into a more coherent picture. We present a literature mining method, RaJoLink, to search for matched themes in unrelated literature that may contribute to a better understanding of complex pathological conditions, such as autism. 214 full text articles on autism, published in PubMed, served as a source of data. Using ontology construction, we identified the main concepts of what is already known about autism. Then, the RaJoLink method, based on Swanson's ABC model, was used to reveal potentially interesting, but not yet investigated, connections between different concepts in research. Among the more interesting concepts identified with RaJoLink in our study were calcineurin and NF-kappaB. Both terms can be linked to neuro-immune abnormalities in the brain of patients with autism. Further research is needed to provide stronger evidence about calcineurin and NF-kappaB involvement in autism. However, the analysis presented confirms that this method could support experts on their way towards discovering hidden relationships and towards a better understanding of the disorder.

Management of a primary health-care network (PHCN) is a difficult task in every country. A suitable monitoring system can provide useful information for PHCN management, especially given a large quantity of health-care data that is produced daily in the network. This paper proposes a methodology for structured development of monitoring systems and a PHCN resource allocation monitoring model based on this methodology. The purpose of the monitoring model is to improve the allocation of health-care resources. The proposed methodology is based on modules that are organized into a hierarchy, where each module monitors a particular aspect of the system. This methodology was used to design a PHCN monitoring model for Slovenia. Specific aspects of the Slovenian PHCN were taken into account such as varying needs of patients from different municipalities, existence of small municipalities having less than 1000 residents, the fact that many patients visit physicians in other municipalities, and that physicians may work at more than one location or organization. The main modules in the model are focused on the overall assessment of the PHCN, monitoring of patients visits to health-care providers (HCPs), physical accessibility of health services, segment of patients in municipalities who have not selected a personal physician, assessment of the availability of HCPs for patients, physicians working on more than one location, and available human resources in the PHCN. Most of the model's components are general and can be adapted for other national health-care systems., (Copyright (c) 2010 John Wiley & Sons, Ltd.)

To support biomedical experts in their knowledge discovery process, we have developed a literature mining method called RaJoLink for identification of relations between biomedical concepts in disconnected sets of articles. The method implements Swanson's ABC model approach for generating hypotheses in a new way. The main novelty is a semi-automated suggestion of candidates for agents a that might be logically connected with a given phenomenon c under investigation. The choice of candidates for a is based on rare terms identified in the literature on c. As rare terms are not part of the typical range of information, which describe the phenomenon under investigation, such information might be considered as unusual observations about the phenomenon c. If literatures on these rare terms have an interesting term in common, this joint term is declared as a candidate for a. Linking terms b between literature on a and literature on c are then searched for in the closed discovery to provide additional supportive evidence for uncovered connections. We have applied the method to the literature on autism and have used MEDLINE as a source of data. Expert evaluation has confirmed that the discovered relations might contribute to a better understanding of autism.

This paper proposes an innovative use of data mining and visualization techniques for decision support in planning and regional-level management of Slovenian public health-care. Data mining and statistical techniques were used to analyze databases collected by a regional Public Heath Institute. We also studied organizational aspects of public health resources in the selected Celje region with the objective to identify the areas that are atypical in terms of availability and accessibility of public health services for the population. The most important step was the detection of outliers and the analysis of availability and accessibility deviations. The results are applicable to health-care planning and support in decision making by local and regional health-care authorities. In addition to the practical results, which are directly useful for decision making in planning of the regional health-care system, the main methodological contribution of the paper are the developed visualization methods that can be used to facilitate knowledge management and decision making processes.

A system of computerized estimation of compatibility of stressors at work and worker's health characteristics is presented. Each characteristic is defined and scored on a specific scale. Incompatible workplace characteristics as related to worker's characteristics are singled out and offered to the user for an ergonomic solution. Work on the system started in 1987. This paper deals with the system's further development, which involves a larger number of topics, changes of the algorithm and presentation of an applicative case. Comparison of the system's results with those of medical experts shows that the use of the system tends to improve the thoroughness and consistency of incompatibility evaluations and consequently to make working ability assessment more objective.