Abstract
This article presents an Artificial Intelligence (AI)-based infrastructure to reduce medication errors while following a treatment plan at home. The system, in particular, assists patients who have some cognitive disability. The AI-based system first learns the skills of a patient using the Actor–Critic method. After assessing patients’ disabilities, the system adopts an appropriate method for the monitoring process. Available methods for monitoring the medication process are a Deep Learning (DL)-based classifier, Optical Character Recognition, and the barcode technique. The DL model is a Convolutional Neural Network (CNN) classifier that is able to detect a drug even when shown in different orientations. The second technique is an OCR based on Tesseract library that reads the name of the drug from the box. The third method is a barcode based on Zbar library that identifies the drug from the barcode available on the box. The GUI demonstrates that the system can assist patients in taking the correct drug and prevent medication errors. This integration of three different tools to monitor the medication process shows advantages as it decreases the chance of medication errors and increases the chance of correct detection. This methodology is more useful when a patient has mild cognitive impairment.
Highlights
A trend of shifting more and more patients from hospitals to homes for treatment has emerged recently [1]
We propose an Artificial Intelligence (AI)-based system that assists patients and the elderly during the medication process at home in order to minimize medication errors
An Reinforcement Learning (RL) problem is first modeled as a Markov Decision Process (MDP), as shown in Figure 1, and an appropriate RL algorithm is employed based on the dynamics of the underlying environment
Summary
A trend of shifting more and more patients (not with several symptoms) from hospitals to homes for treatment has emerged recently [1]. The risks and consequent impacts of the medication errors have been reviewed in different surveys [5,10] These studies emphasize the need for systems that are able to assist the elderly and patients during medical treatment at home. The major component of the proposed work is the RL agent that integrates multiple AI methods (DL, OCR, and barcode) and can provide assistance to the elderly and to patients with cognitive disabilities in their medical treatment at home. The advantage of integrating three different methods to monitor the medication process is that it decreases the chance of medication errors and increases the chance of correct detection This methodology is more useful when a patient has mild cognitive impairment.
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