An Artificial Neural Network Classification of Prescription Nonadherence

This study investigates the use of artificial neural networks (ANNs) to classify reasons for medication

nonadherence. A survey method is used to collect individual reasons for nonadherence to treatment

plans. Seven reasons for nonadherence are identified from the survey. ANNs using backpropagation

learning are trained and validated to produce a nonadherence classification model. Most patients

identified multiple reasons for nonadherence. The ANN models were able to accurately predict almost

63 percent of the reasons identified for each patient. After removal of two highly common nonadherence

reasons, new ANN models are able to identify 73 percent of the remaining nonadherence reasons. ANN

models of nonadherence are validated as a reliable medical informatics tool for assisting healthcare

providers in identifying the most likely reasons for treatment nonadherence. Physicians may use

the identified nonadherence reasons to help overcome the causes of nonadherence for each patient.