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

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

Stud Health Technol Inform. 2015;216:741-745

Authors: Salvini R, da Silva Dias R, Lafer B, Dutra I

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.

PMID: 26262150 [PubMed - as supplied by publisher]

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

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

Stud Health Technol Inform. 2015;216:741-745

Authors: Salvini R, da Silva Dias R, Lafer B, Dutra I

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.

PMID: 26262150 [PubMed - as supplied by publisher]

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