A Multi-Relational Model for Depression Relapse in Patients with Bipolar Disorder

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art_SALVINI_A_MultiRelational_Model_for_Depression_Relapse_in_Patients_2015.PDF (144.23 KB)
Citações na Scopus
9
Tipo de produção
conferenceObject
Data de publicação
2015
Editora
IOS PRESS
DOI
Indexadores
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Autores
SALVINI, Rogerio
DIAS, Rodrigo da Silva
LAFER, Beny
DUTRA, Ines
Autor de Grupo de pesquisa
Editores
Coordenadores
Organizadores
Citação
MEDINFO 2015: EHEALTH-ENABLED HEALTH, v.216, p.741-745, 2015
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
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.
Palavras-chave
Bipolar Disorder, Depression Relapse, Multi-relational Model, Inductive Logic Programming, Machine Learning
URI
https://observatorio.fm.usp.br/handle/OPI/31463
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Comunicações em Eventos - FM/MPS
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