{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T05:16:33Z","timestamp":1779167793009,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>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\u2013Critic method. After assessing patients\u2019 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.<\/jats:p>","DOI":"10.3390\/jsan11010013","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T21:22:15Z","timestamp":1644441735000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["An AI-Empowered Home-Infrastructure to Minimize Medication Errors"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0815-4883","authenticated-orcid":false,"given":"Muddasar","family":"Naeem","sequence":"first","affiliation":[{"name":"Institute of High Performance Computing and Networking, National Research Council of Italy, 80131 Napoli, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8177-032X","authenticated-orcid":false,"given":"Antonio","family":"Coronato","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing and Networking, National Research Council of Italy, 80131 Napoli, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1177\/1941874414540683","article-title":"Transitional care strategies from hospital to home: A review for the neurohospitalist","volume":"5","author":"Rennke","year":"2015","journal-title":"Neurohospitalist"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alzahrani, N. 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