Predicting treatment recommendations in postmenopausal osteoporosis

• Published in: Journal of biomedical informatics Vol. 118; p. 103780
• Main Authors: Bonaccorsi, G., Giganti, M., Nitsenko, M., Pagliarini, G., Piva, G., Sciavicco, G.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2021
• Subjects: Clinical decision support system; Keywords: Osteoporosis treatment; Machine learning; Rule extraction; Clinical decision support system; Machine learning; Keywords: Osteoporosis treatment; Rule extraction
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2021.103780

[Display omitted] •We present an original CDSS for the management of osteoporotic pa-tients (DXA, clinical history, diagnosis, treatment recommendation).•We present an intelligent agent on our CDSS, which is able to help physician’s decisions for treatment recommendation, based on similar patients in the data base. The system is original, and self-adapting.•We describe an experiment on 2052 postmenopausal women, designed to assess the accuracy of treatment recommendation suggestions. 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%.