Smart technologies and its application for medical/healthcare services

Izonin, Ivan; Ribino, Patrizia; Ebrahimnejad, Ali; Quinde, Mario

doi:10.1007/s40860-023-00201-z

J Reliable Intell Environ

2023

DOI: 10.1007/s40860-023-00201-z

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Smart technologies and its application for medical/healthcare services

Ivan Izonin

Patrizia Ribino

Ali Ebrahimnejad

et al.

Abstract: Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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2024

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Advancing Patient Care with an Intelligent and Personalized Medication Engagement System

Ismail,

Naeem,

Syed

et al. 2024

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Therapeutic efficacy is affected by adherence failure as also demonstrated by WHO clinical studies that 50–70% of patients follow a treatment plan properly. Patients’ 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’s 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’s 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.

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Advancing Patient Care with an Intelligent and Personalized Medication Engagement System

Ismail,

Naeem,

Syed

et al. 2024

Information

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show abstract

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Smart technologies and its application for medical/healthcare services

Abstract: Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Cited by 1 publication

References 6 publications

Advancing Patient Care with an Intelligent and Personalized Medication Engagement System

Advancing Patient Care with an Intelligent and Personalized Medication Engagement System

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