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- W206870228 abstract "As recommender systems have proven their effectiveness in other areas, it is aimed to transfer this approach for use in medicine. Particularly, the diagnoses of physicians made 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 in order to reduce the risk to patients. Recommendation is mainly based on finding known patterns similar to a case under consideration. To search for such patterns in rather large databases, a weighted similarity distance is employed, which is specially derived for medical knowledge. For collaborative filtering an incremental algorithm, called W-InCF, is used working with the Mahalanobis distance and fuzzy membership. W-InCF consists of a learning phase, in which a cluster model of patients’ medical history is constructed incrementally, and a prediction phase, in which the medical pattern of each patient considered is compared with the model to determine the most similar cluster. Fuzzy sets are employed to cope with possible confusion of decision making on overlapping clusters. The degrees of membership to these fuzzy sets is expressed by a weighted Mahalanobis radial basis function, and the weights are derived from risk factors identified by experts. The algorithm is validated using data on cephalopelvic disproportion." @default.
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- W206870228 date "2012-01-01" @default.
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- W206870228 title "Determining Pattern Similarity in a Medical Recommender System" @default.
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- W206870228 doi "https://doi.org/10.1007/978-3-642-34679-8_11" @default.
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