人工智能支持的家庭基础设施可最大限度地减少用药错误

Muddasar Naeem, A. Coronato
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引用次数: 7

摘要

本文介绍了一种基于人工智能(AI)的基础设施,以减少在家中遵循治疗计划时的用药错误。该系统尤其能帮助有认知障碍的患者。基于人工智能的系统首先使用Actor-Critic方法学习患者的技能。在对患者的残疾进行评估后,系统采用合适的方法进行监测过程。监测用药过程的可用方法是基于深度学习(DL)的分类器、光学字符识别和条形码技术。DL模型是一个卷积神经网络(CNN)分类器,即使在不同的方向上显示,也能够检测到药物。第二种技术是基于Tesseract库的OCR,它从盒子中读取药物的名称。第三种方法是基于Zbar库的条形码,从盒子上可用的条形码识别药物。图形用户界面表明,该系统可以帮助患者正确用药,防止用药错误。集成三种不同的工具来监控用药过程显示出优势,因为它减少了用药错误的机会并增加了正确检测的机会。当患者有轻度认知障碍时,这种方法更有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An AI-Empowered Home-Infrastructure to Minimize Medication Errors
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.
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