Data Mining Techniques in Medical Informatics

The advent of high-performance computing has benefited various disciplines in finding practical solutions to their problems, and our health care is no exception to this. Signal processing, image processing, and data mining tools have been developed for effective analysis of medical information, in order to help clinicians in making better diagnosis for treatment purposes. Data mining has become a fundamental methodology for computing applications in medical informatics. Progress in data mining applications and its implications are manifested in the areas of information management in healthcare organizations, health informatics, epidemiology, patient care and monitoring systems, assistive technology, large-scale image analysis to information extraction and automatic identification of unknown classes. Various algorithms associated with data mining have significantly helped to understand medical data more clearly, by distinguishing pathological data from normal data, for supporting decision-making as well as visualization and identification of hidden complex relationships between diagnostic features of different patient groups. There are nine papers in this Special issue, covering different areas in medical informatics. Paper 1 proposes a metabonomic study applied to medical diagnosis. Metabolomics and metabonomics belong to the "-omics " sciences. Particularly, metabonomic correlates the metabolic fingerprint to characteristics of specific patient categories. Usually, metabonomic studies are conducted by in-vitro spectroscopy. The aim of this study was to apply data-mining metabonomic techniques to the clinical diagnosis of genetic mutations in migraine sufferers. This is one of the first applications of advanced data-mining techniques to a mixed database consisting of hematochemical, instrumental, and genetic variables. There has been an effort to use motion-related surface vibration, to detect independent finger motions is in practice. Accelerometers have been used in a finger tapping experiment to collect the finger motion related mechanical vibration patterns. The extracted time-domain and frequency-domain features were fed to back-propagation neural networks, to classify different finger motions. The insights provided in paper 2 will be helpful for prosthetic hand control. Microscopic imaging is ubiquitous in several medical informatics disciplines, including but not limited to cancer informatics, neuro-informatics, and other emerging health informatics disciplines. The decision support applications frequently require the sensitive and specific detection of pathological changes in cells, which further require the accurate measurement of their geometric parameters. In paper 3, Du et al. have suggested that due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. They have evaluated the …