ArticleNeuropsychiatric disease and treatment2019
Fluvoxamine treatment response prediction in obsessive-compulsive disorder: association rule mining approach.
Article in Neuropsychiatric disease and treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- The Effects of Demographic Characteristics, Memory Deficits, and Executive Functioning Impairments in Failure to Respond to Fluvoxamine among Patients with Obsessive-Compulsive Disorder in Iran.Medical journal of the Islamic Republic of Iran · 2022Article
- Response to Fluvoxamine in the Obsessive-Compulsive Disorder Patients: Bayesian Ordinal Quantile Regression.Clinical practice and epidemiology in mental health : CP & EMH · 2021Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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Abstract
backgroundObsessive-compulsive disorder (OCD) is a debilitating psychiatric disorder characterized by intrusive thoughts or repetitive behaviors. Clinicians use serotonin reuptake inhibitors (SRIs) for OCD treatment, but 40%-60% of the patients do not respond to them adequately. Here, we described an association rule mining approach for treatment response prediction using an Iranian OCD data set. PATIENTS AND
methodsThree hundred and thirty OCD patients fulfilling
resultsResults showed that low obsession and compulsion severities, family history of mental illness, illness duration less than 5 years, being married, and female were the most associated variables with responsiveness to fluvoxamine pharmacotherapy. Meanwhile, if an OCD patient reported a family history of mental illness and his/her illness duration was less than 5 years, he/she responded to 12-week fluvoxamine pharmacotherapy with the probability of 91%. We also found useful and applicable rules for resistant and refractory patients.
conclusionThis is the first study where association rule mining approach was used to extract predicting rules for treatment response in OCD. Application of this method in personalized medicine may help clinicians in taking the right therapeutic decision.
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