Abstract: A method for adherence prediction, comprising the steps of isolating a training dataset and a test dataset from the full dataset; splitting the training dataset into training folds and a validation fold; training, over a plurality of trials, one or more models on each of the training folds for each of one or more parameter configurations correlated to said model, each of the models configured to classify a target variable as an indicator of whether a patient will complete a treatment; validating, for each of the one or more models for a given trial of the plurality of trials, a given model of the one or more models on the validation fold; recording classifications scores for each of the one or more models; selecting a model from the one or more models, the selected model having a top classification score; and retraining the selected model on the full dataset.

Abstract: A system for electronic health record (EHR) management and dynamic treatment adherence prediction comprising a set of computer-executable instructions causing the server to generate, via a first client device, a patient-facing interface configured to receive patient information; transmit, from the first client device to the server, the patient information; populate, via the at least one server processor, an EHR of the patient with the patient information; generate, via the at least one server processor, via a treatment adherence algorithm based at least on a portion of the patient information, an adherence probability of the patient, wherein the adherence probability is the likelihood that the patient will complete a treatment; transmit, from the server to a second client device, the EHR and the adherence probability; generate, via the second client device, a provider-facing interface comprising the EHR of the patient, the EHR comprising the adherence probability of the patient.