{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T05:15:49Z","timestamp":1740287749611,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>Bipolar Disorder (BD) is a chronic and disabling disease that usually appears around 20 to 30 years old. Patients who suffer with BD may struggle for years to achieve a correct diagnosis, and only 50% of them generally receive adequate treatment. In this work we apply a machine learning technique called Inductive Logic Programming (ILP) in order to model relapse and no-relapse patients in a first attempt in this area to improve diagnosis and optimize psychiatrists' time spent with patients. We use ILP because it is well suited for our multi-relational dataset and because a human can easily interpret the logical rules produced. Our classifiers can predict relapse cases with 92% Recall and no-relapse cases with 73% Recall. The rules and variable theories generated by ILP reproduce some findings from the scientific literature. The generated multi-relational models can be directly interpreted by clinicians and researchers, and also open space to research biological mechanisms and interventions.<\/jats:p>","DOI":"10.3233\/978-1-61499-564-7-741","type":"book-chapter","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T23:19:12Z","timestamp":1740266352000},"source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Relational Model for Depression Relapse in Patients with Bipolar Disorder"],"prefix":"10.3233","author":[{"family":"Salvini Rogerio","sequence":"additional","affiliation":[]},{"family":"da Silva Dias Rodrigo","sequence":"additional","affiliation":[]},{"family":"Lafer Beny","sequence":"additional","affiliation":[]},{"family":"Dutra In&ecirc;s","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2015: eHealth-enabled Health"],"original-title":[],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T23:40:30Z","timestamp":1740267630000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-563-0&spage=741&doi=10.3233\/978-1-61499-564-7-741"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-564-7-741","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2015]]}}}