We designed, implemented, and tested a clinical decision support system at the Research Center for the Study of Menopause and Osteoporosis within the University of Ferrara (Italy). As an independent module of our system, we implemented an original machine learning system for rule extraction, enriched with a hierarchical extraction methodology and a novel rule evaluation technique. Such a module is used in everyday operation protocol, and it allows physicians to receive suggestions for prevention and treatment of osteoporosis. In this paper, we design and execute an experiment based on two years of data, in order to evaluate and report the reliability of our suggestion system. Our results are encouraging, and in some cases reach expected accuracies of around 90%.
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| http://dx.doi.org/10.1016/j.jbi.2021.103780 | DOI Listing |
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