Article
An Artificial Neural Network Classification of Prescription Nonadherence
International Journal of Healthcare Information Systems and Informatics
(2017)
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.
Keywords
- Artificial Neural Network,
- Backpropagation,
- drug,
- EHR,
- medication,
- medicine,
- Nigeria,
- nonadherence,
- pharmaceutical,
- prescription
Disciplines
Publication Date
January, 2017
DOI
10.4018/IJHISI.2017010101
Citation Information
Steven Walczak and Senanu Okuboyejo. "An Artificial Neural Network Classification of Prescription Nonadherence" International Journal of Healthcare Information Systems and Informatics Vol. 12 Iss. 1 (2017) p. 1 - 13 Available at: http://works.bepress.com/steven-walczak/69/