USING FUZZY-LOGIC TO PREDICT RESPONSE TO CITALOPRAM IN ALCOHOL DEPENDENCE

Introduction: The prediction of patient response to new pharmacotherap ies for alcohol dependence has usually not been successful with standa rd statistical techniques. We hypothesized that fuzzy logic, a qualita tive computational approach, could predict response to 40 mg/day cital opram 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 (i t = 28). The output variable, ''response,'' was the percentage decreas e in alcohol intake from baseline. Input variables included age, gende r, baseline alcohol intake, and levels of anxiety, depression, alcohol dependence, and alcohol-related problems. Results: A fuzzy rulebase w as created from the data of 26 randomly chosen patients who received 4 0 mg/day citalopram and was used to predict the responses of the remai ning eight patients. Eight rules related response with depression, anx iety, 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 th e data of 28 randomly chosen patients who received 40 mg/day citalopra m and a brief psychosocial intervention and was used to predict the re sponses of the remaining five patients. Six rules related response wit h age, anxiety, depression, alcohol dependence, and baseline alcohol i ntake with good predictive performance (RMSE = 6.4; ME = -1.5; r = 0.9 6; p < 0.01). Conclusions: This study indicates that fuzzy logic model ing can predict response to pharmacotherapies for alcohol dependence.