Medication recommendation system for online pharmacy using an adaptive user interface

Abstract

Background

This article proposes a prototype of a user-adaptive system for helping patients to obtain their ambulatory prescribed medications when purchasing online in a more convenient manner than traditional methods, and the adoption of artificial intelligence to achieve improvements. The system developed simulates an online pharmacy with an introductory adaptive user interface using Bayesian user modeling for predicting the medication needs of patients. This program is used to show its step-by-step design and functioning.

Methods

The introductory adaptive user interface was developed on Visual C++ of Microsoft Visual Studio. The patient model acquisition and application implementing the learning and inference was performed with a Bayesian Network. The Bayesian network was elaborated with the GeNIe Modeler software, Version 2.3.R4, provided by BayesFusion, LLC. Synthetic data from a synthetically generated dataset of anonymous patients was used. The performance of the system was evaluated through simulations using testing data from the synthetic dataset. The Accuracy of predictions was analyzed.

Results

The Average accuracy was estimated with the average correct recommendations of medications, for different numbers of purchased medications per session. The Average accuracy increased with the number of purchased medications, from 86.3529% up to 92.5303%. The Average wrong recommendations decreased with the increase in the number of purchased medications, from an average of 3.4117 up to 1.5686.

Conclusion

The system quickly and consistently attained high accuracy in predicting the medication categories needed by the patients, potentially being able to save time and effort for the patients by relying on the system's recommendations.