{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:22:32Z","timestamp":1776334952354,"version":"3.51.2"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T00:00:00Z","timestamp":1728000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union-Next-GenerationEU-National Recovery and Resilience","award":["Plan-NRRP-M4C1-I 1.1"],"award-info":[{"award-number":["Plan-NRRP-M4C1-I 1.1"]}]},{"name":"European Union-Next-GenerationEU-National Recovery and Resilience","award":["CALL PRIN 2022 PNRR D.D. 1409 14-09-2022"],"award-info":[{"award-number":["CALL PRIN 2022 PNRR D.D. 1409 14-09-2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Therapeutic efficacy is affected by adherence failure as also demonstrated by WHO clinical studies that 50\u201370% of patients follow a treatment plan properly. Patients\u2019 failure to follow prescribed drugs is the main reason for morbidity and mortality and more cost of healthcare services. Adherence to medication could be improved with the use of patient engagement systems. Such engagement systems can include a patient\u2019s preferences and beliefs in the treatment plans, resulting in more responsive and customized treatments. However, one key limitation of the existing engagement systems is their generic applications. We propose a personalized framework for patient medication engagement using AI methods such as Reinforcement Learning (RL) and Deep Learning (DL). The proposed Personalized Medication Engagement System (PMES) has two major components. The first component of the PMES is based on an RL agent, which is trained on adherence reports and later utilized to engage a patient. The RL agent, after training, can identify each patient\u2019s patterns of responsiveness by observing and learning their response to signs and then optimize for each individual. The second component of the proposed system is based on DL and is used to monitor the medication process. The additional feature of the PMES is that it is cloud-based and can be utilized anywhere remotely. Moreover, the system is personalized as the RL component of PMES can be trained for each patient separately, while the DL part of the PMES can be trained for a given medication plan. Thus, the advantage of the proposed work is two-fold, i.e., RL component of the framework improves adherence to medication while the DL component minimizes medication errors.<\/jats:p>","DOI":"10.3390\/info15100609","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T12:02:20Z","timestamp":1728043340000},"page":"609","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Advancing Patient Care with an Intelligent and Personalized Medication Engagement System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7421-629X","authenticated-orcid":false,"given":"Ahsan","family":"Ismail","sequence":"first","affiliation":[{"name":"Department of Electronics, Quaid-i-Azam University Islamabad, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0815-4883","authenticated-orcid":false,"given":"Muddasar","family":"Naeem","sequence":"additional","affiliation":[{"name":"Research Center on ICT Technologies for Healthcare and Wellbeing, Universit\u00e0 Telematica Giustino Fortunato, 82100 Benevento, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0123-3554","authenticated-orcid":false,"given":"Madiha Haider","family":"Syed","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, Quaid-i-Azam University Islamabad, Islamabad 44000, Pakistan"}]},{"given":"Musarat","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Electronics, Quaid-i-Azam University Islamabad, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8177-032X","authenticated-orcid":false,"given":"Antonio","family":"Coronato","sequence":"additional","affiliation":[{"name":"Research Center on ICT Technologies for Healthcare and Wellbeing, Universit\u00e0 Telematica Giustino Fortunato, 82100 Benevento, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1111\/jch.12953","article-title":"Antihypertensive medication adherence and blood pressure control among central Alabama veterans","volume":"19","author":"Piercefield","year":"2017","journal-title":"J. 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