As recommender systems have proven their usefulness in e-commerce by providing personalised recommendations, the approach is to be transferred to medicine. Particularly, the diagnoses made by physicians in rural hospitals of developing countries, in remote areas or in situations of uncertainty are to be complemented by machine recommendations drawing on large bases of expert knowledge to reduce the risk to patients. To this end, a database of patients' medical history and a cluster model is maintained centrally. The model is constructed incrementally by a combination of collaborative and knowledge-based filtering, in the course of which it permanently widens its knowledge base on a medical area given. To give a recommendation, the model's cluster matching the diagnostic pattern of a considered patient best is sought. The therapy actually applied after the recommendation and its subsequently observed consequences are fed back for model updating. Readily available personal digital accessories can be used for remote data entry and recommendation display as well as for communication with the central site. The approach is validated in the area of obstetrics and gynecology using data on cephalopelvic disproportion. © IFAC.
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