Abstract: A method for training an adherence model, the method including: extracting data for a group of individuals (510), wherein the extracted data includes demographic data (205) and clinical data (210); training a linear regression model (520) using a set of hyperparameter pairs (L1, Alpha) (515), wherein the linear regression model produces an adherence index based upon the extracted data, further including: for each hyperparameter pair (L1, Alpha) in the set of hyperparameter pairs, training the linear regression model using a training data set to produce a linear regression model for each hyperparameter pair (L1, Alpha) and calculating a performance metric R2 for the resulting model based upon a validation data set (525), wherein the training data set is a subset of the extracted data and the validation data set is a subset of the extracted data that is different from the training data set; and identifying the linear regression model with the largest performance metric R2 (530).

Abstract: Disclosed herein is a method and system for evaluating compliance of standard clinical guidelines in medical treatments of one or more patients. Electronic Health Records (EHRs) of the patients are received and aggregated to obtain patient-specific EHRs. Subsequently, the patients are clustered into groups based on disease conditions and socio-demographic characteristics of patients. Further, deviations in the medical treatments provided to the patients are detected by comparing the medical treatments with standard clinical guidelines, for evaluating compliance of the standard clinical guidelines with the medical treatments. In an embodiment, the method of present disclosure helps in detecting care gaps among the medical treatments provided to the patients, and thereby helps in providing improvised and/or patient-specific medical assistance to the patients.