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Title: A Multi-Relational Model for Depression Relapse in Patients with Bipolar Disorder
Authors: SALVINI, RogerioDIAS, Rodrigo da SilvaLAFER, BenyDUTRA, Ines
Citation: MEDINFO 2015: EHEALTH-ENABLED HEALTH, v.216, p.741-745, 2015
Abstract: 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.
Appears in Collections:

Comunicações em Eventos - FM/MPS
Departamento de Psiquiatria - FM/MPS

Comunicações em Eventos - HC/IPq
Instituto de Psiquiatria - HC/IPq

Comunicações em Eventos - LIM/21
LIM/21 - Laboratório de Neuroimagem em Psiquiatria

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