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Using fuzzy logic to predict response to citalopram in alcohol dependence
Author(s) -
Naranjo Claudio A.,
Bremner Karen E.,
Bazoon Mehdi,
Turksen I. Burhan
Publication year - 1997
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1016/s0009-9236(97)90070-9
Subject(s) - citalopram , psychosocial , alcohol dependence , anxiety , alcohol , depression (economics) , medicine , psychology , psychiatry , antidepressant , chemistry , biochemistry , macroeconomics , economics
The prediction of patient response to new pharmacotherapies for alcohol dependence has usually not been successful with standard statistical techniques. We hypothesized that fuzzy logic, a qualitative computational approach, could predict response to 40 mg/day citalopram and 40 mg/day citalopram with a brief psychosocial intervention in alcohol‐dependent patients. Methods Two data sets were formed with patients from our studies who received 40 mg/day citalopram alone ( n = 34) or 40 mg/day citalopram and a brief psychosocial intervention ( n = 28). The output variable, “response,” was the percentage decrease in alcohol intake from baseline. Input variables included age, gender, baseline alcohol intake, and levels of anxiety, depression, alcohol dependence, and alcohol‐related problems. Results A fuzzy rulebase was created from the data of 26 randomly chosen patients who received 40 mg/day citalopram and was used to predict the responses of the remaining eight patients. Eight rules related response with depression, anxiety, alcohol dependence, alcohol‐related problems, age, and baseline alcohol intake. The average magnitude of the error in the predictions (RMSE) was 2.6 with a bias (ME) of 0.6. Predicted and actual response correlated ( r = 0.99; p < 0.001). A fuzzy rulebase was created from the data of 28 randomly chosen patients who received 40 mg/day citalopram and a brief psychosocial intervention and was used to predict the responses of the remaining five patients. Six rules related response with age, anxiety, depression, alcohol dependence, and baseline alcohol intake with good predictive performance (RMSE = 6.4; ME = −1.5; r = 0.96; p < 0.01). Conclusions This study indicates that fuzzy logic modeling can predict response to pharmacotherapies for alcohol dependence. Clinical Pharmacology & Therapeutics (1997) 62 , 209–224; doi: