Knowledge discovery for advanced clinical data management and analysis.
Knowledge discovery is a broad research field in which methods are developed to support discovery of novel and potentially useful knowledge from clinical databases and registers in systems for patient care. However, the techniques available are not readily applicable in medical domains, due to, among other reasons, low user friendliness and lack of proper methodological background. Data mining approaches to be explored and improved are predictive modelling, segmentation, dependency modelling, summarization, and change and deviation detection/modelling (in data or knowledge). Another and original contribution of the research is to build up efficient feedback loops. Human experts and available domain expert systems could provide suggestions as how to improve all major steps in the knowledge discovery process such as evaluation of knowledge, choice of data mining methods and data input. A long tradition of collecting and maintaining clinical and administrative data could be found in fields of oncology, cardiology, coronary surgery, social and primary health care medicine. All these areas, that gather data over long periods of time, could benefit from knowledge discovery.
Babic
A
A
Dept. of Biomedical Engineering, Linkoping University, Sweden.
D001185:Artificial Intelligence; D003625:Data Collection; D003628:Database Management Systems; D005103:Expert Systems; D005246:Feedback; D006801:Humans; D016347:Medical Records Systems, Computerized; D016571:Neural Networks, Computer; D012106:Research