Okuboyejo, Senanu An Artificial Neural Network Classification of Prescription Nonadherence. International Journal of Healthcare Information Systems and Informatics, 12 (1). pp. 1-13.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Dr Sena Okuboyejo |
| Date Deposited: | 18 Sep 2018 11:53 |
| Last Modified: | 18 Sep 2018 11:53 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/11713 |
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