A method of building a multiplicity of predictive computerized models for predicting the efficacy of anti- epilepsy drug treatment regimens comprising: a) providing electronic health records data; b) constructing a patient cohort from the electronic health records data by selecting patients based on a defined target variable indicating anti-epilepsy drug treatment regimen efficacy; c) constructing a set features found in or derived from the electronic health records data, wherein said features include: (i) demographic features representing basic demographics of the patient such as age, gender and the geographic information of the patient; (ii) hospitalization features that include type of visit, such as inpatient visit, outpatient visit or ER visit, and length of stay; (iii) diagnosis and procedures features including features representing the different diagnosis and medical procedures of patients recorded by clinicians in multiple visits; and (iv) treatment features including both anti-epileptic drugs and non-anti-epileptic drugs prescribed to the patient including the class of the drugs; d) selecting features by electronically processing the set of constructed features based on the patient cohort to select a subset of the constructed features that are predictive for anti-epilepsy drug treatment regimen efficacy for inclusion in predictive computerized models configured for generating predictions representative of efficacy for a plurality of anti-epilepsy drug treatment regimens, including performing a statistical test on the features with respect to each of the anti-epilepsy drug treatment regimens to identify which of the features have a statistical significance value with respect to the respective anti-epilepsy drug treatment regimen efficacy within a predetermined range; and e) training each of the predictive computerized models to generate predictions representative of efficacy for a plurality of anti-epilepsy drug treatment regimens for the patients based on the defined target variable indicating anti-epilepsy drug treatment regimen efficacy, each of the predictive computerized models being configured for generating predictions representative of the efficacy of one of the anti-epilepsy drug treatment regimens for a patient based on the identified features. a) providing electronic health records data; b) constructing a patient cohort from the electronic health records data by selecting patients based on a defined target variable indicating anti-epilepsy drug treatment regimen efficacy; c) constructing a set features found in or derived from the electronic health records data, wherein said features include: (i) demographic features representing basic demographics of the patient such as age, gender and the geographic information of the patient; (ii) hospitalization features that include type of visit, such as inpatient visit, outpatient visit or ER visit, and length of stay; (iii) diagnosis and procedures features including features representing the different diagnosis and medical procedures of patients recorded by clinicians in multiple visits; and (iv) treatment features including both anti-epileptic drugs and non-anti-epileptic drugs prescribed to the patient including the class of the drugs; (i) demographic features representing basic demographics of the patient such as age, gender and the geographic information of the patient; (ii) hospitalization features that include type of visit, such as inpatient visit, outpatient visit or ER visit, and length of stay; (iii) diagnosis and procedures features including features representing the different diagnosis and medical procedures of patients recorded by clinicians in multiple visits; and (iv) treatment features including both anti-epileptic drugs and non-anti-epileptic drugs prescribed to the patient including the class of the drugs; d) selecting features by electronically processing the set of constructed features based on the patient cohort to select a subset of the constructed features that are predictive for anti-epilepsy drug treatment regimen efficacy for inclusion in predictive computerized models configured for generating predictions representative of efficacy for a plurality of anti-epilepsy drug treatment regimens, including performing a statistical test on the features with respect to each of the anti-epilepsy drug treatment regimens to identify which of the features have a statistical significance value with respect to the respective anti-epilepsy drug treatment regimen efficacy within a predetermined range; and e) training each of the predictive computerized models to generate predictions representative of efficacy for a plurality of anti-epilepsy drug treatment regimens for the patients based on the defined target variable indicating anti-epilepsy drug treatment regimen efficacy, each of the predictive computerized models being configured for generating predictions representative of the efficacy of one of the anti-epilepsy drug treatment regimens for a patient based on the identified features.