An Artificial Neural Network Classification of Prescription Nonadherence
Document Type
Article
Publication Date
3-2017
Keywords
Artificial Neural Network (ANN), backpropagation, drug, EHR, medication, medicine, Nigeria, Nonadherence, pharmaceutical, prescription
Digital Object Identifier (DOI)
https://doi.org/10.4018/IJHISI.2017010101
Abstract
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.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
International Journal of Healthcare Information Systems and Informatics, v. 12, issue 1, art. 1
Scholar Commons Citation
Walczak, Steven and Okuboyejo, Senanu R., "An Artificial Neural Network Classification of Prescription Nonadherence" (2017). School of Information Faculty Publications. 337.
https://digitalcommons.usf.edu/si_facpub/337
