A Multi-Relational Model for Depression Relapse in Patients with Bipolar Disorder.
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.
Instituto de Informática, Universidade Federal de Goiás, Goiânia, GO, Brazil.
Rogerio
R
Salvini
Bipolar Research Program, Department and Institute of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil.
Rodrigo
R
da Silva Dias
Bipolar Research Program, Department and Institute of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil.
Beny
B
Lafer
CRACS & INESC TEC-Porto LA, University of Porto, Porto, Portugal.
Inês
I
Dutra
D000465:Algorithms; D001714:Bipolar Disorder / Q000150:complications / Q000175:diagnosis*; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003863:Depression / Q000175:diagnosis* / Q000209:etiology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D012008:Recurrence; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity