Case-based reasoning to explain medical model exceptions.
In medicine many exceptions occur. In medical practise and in knowledge-based systems too, it is necessary to consider them and to deal with them appropriately. In medical studies and in research exceptions shall be explained. We present a system that helps to explain cases that do not fit into a theoretical hypothesis. Our starting points are situations where neither a well-developed theory nor reliable knowledge nor a proper case base is available. So, instead of reliable theoretical knowledge and intelligent experience, we have just some theoretical hypothesis and a set of measurements. In this paper, we propose to combine CBR with a statistical model. We use CBR to explain those cases that do not fit the model. The case base has to be set up incrementally, it contains the exceptional cases, and their explanations are the solutions, which can be used to help to explain further exceptional cases.
Schmidt
Rainer
R
Institute for Medical Informatics and Biometry, University of Rostock, Germany.
Vorobieva
Olga
O
D001185:Artificial Intelligence; D003625:Data Collection; D020000:Decision Support Systems, Clinical; D004734:Energy Metabolism / Q000502:physiology; D015444:Exercise / Q000502:physiology; D005103:Expert Systems; D006801:Humans; D051188:Knowledge Bases; D016347:Medical Records Systems, Computerized; D016571:Neural Networks, Computer; D010100:Oxygen / Q000097:blood; D010281:Parathyroid Hormone / Q000097:blood; D010809:Physical Fitness / Q000502:physiology*; D006435:Renal Dialysis; D012984:Software
D010281:Parathyroid Hormone; D010100:Oxygen