Irs-T2d

Mahmoud, Nesma; Elbeh, Heba

doi:10.1145/2908446.2908495

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…The challenges identified include data handling, data ownership, throughput restriction, knowledge gap, social challenges, standardization challenges, storage capacity, and security of data, scalability, cost, and implementation of BT. Another drug recommendation system developed for managing type 2 diabetes mellitus is IRS-T2D Mahmoud and Elbeh (2016). Using rulebased decision-making models, ontologies, and SWT, this system combines patient data such as HbA1c targets with other relevant information.…”

Section: Medicine and Drug Recommender Systemsmentioning

confidence: 99%

“…Content-based recommenders with machine-readable annotations are used on semantic webs. In order to compare information based on demographics, geography, and medical conditions, semantic clustering is used Mahmoud and Elbeh (2016), assisting in choosing the best course of action. The use of RS technology has been investigated by various researchers in response to the COVID-19 pandemic.…”

Section: Vaccine Recommender Systemsmentioning

confidence: 99%

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Reinforcement learning and blockchain‐based intelligent and secure vaccine recommender system

Sreenu,

Gupta,

Jatoth

et al. 2023

Expert Systems

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By combining blockchain technology (BT) and reinforcement learning (RL), the proposed work addresses the difficulties associated with vaccine recommendations. The need for individualized recommendations is obvious as vaccine schedules become more complicated and there are more vaccines available. The proposed work presents a novel approach that combines the adaptability of RL with the security, privacy, and transparency of BT. Layers for data processing, application, consensus, and smart contracts are included in the system architecture. It provides user‐managed secure access, individualized vaccine recommendations, and decentralized data storage. The system aims to improve public health outcomes by using smart contracts to automate procedures and RL to improve recommendations. Using 10‐fold cross‐validation on the Vaccine Adverse Event Reporting System (VAERS) dataset, the experimental study verifies the performance of the system. The study focuses on metrics like accuracy, sensitivity, specificity, and F‐measure when contrasting the proposed model with current solutions. The ability of proposed model to offer precise and well‐informed vaccine recommendations is demonstrated by its consistent outperformance of competitors in accuracy and sensitivity.

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Section: Medicine and Drug Recommender Systemsmentioning

confidence: 99%

Section: Vaccine Recommender Systemsmentioning

confidence: 99%

Reinforcement learning and blockchain‐based intelligent and secure vaccine recommender system

Sreenu,

Gupta,

Jatoth

et al. 2023

Expert Systems

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“…IRS-T2D, a recommendation system for personalized treatment of type 2 diabetes, based on ontology and Semantic Web Rule Language (SWRL), was proposed by Mahmoud and Elbeh (2016). The official documents for type 2 diabetes management were used as criteria and applied to create ontologies.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Recommendation Model for Drug Selection

KHARMA,

SHAMBOUR,

HUSSEIN

2023

SIC

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Medical errors associated with medication pose significant threats to patients' safety, primarily due to the abundance of drug information available on various online healthcare platforms, leading to challenges in identifying relevant drugs. To address this issue, drug recommendation systems have been developed to assist in selecting appropriate medications for specific medical conditions. Collaborative filtering approaches have been widely used to generate personalized recommendations for various applications. They are easy to implement, debug, and provide justifiable reasoning for recommended items, which is not readily accessible in several other recommendation approaches. Regardless of their success, they still need further enhancements to address challenges related to insufficient rating data, such as data sparsity and new item problems. This paper proposes a drug recommendation model that effectively employs drug taxonomy and multi-criteria collaborative filtering to tackle these challenges. Drug taxonomy enhances recommendation quality by offering a more organized and granular representation of drugs, while multi-criteria rating captures the patients' preferences more accurately, enabling accurate recommendations that better match the patient's specific preferences. Experiments conducted on a real-world drug multi-criteria rating dataset demonstrate that the proposed model outperforms baseline recommendation approaches in addressing these challenges and improving prediction accuracy and coverage, making it a valuable tool to assist patients in selecting relevant drugs for their specific medical conditions.

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“…Mahmoud and Elbeh () proposed the IRS‐T2D, an ontology‐and‐SWRL‐based RS for customizing the treatment of Type 2 diabetes patients. Two OWL ontologies are built: one for patient profiles and another for antidiabetic drugs based on official documents on management of Type 2 diabetes.…”

Section: Key Phrs Applications and Case Studies In Literaturementioning

confidence: 99%

A survey on semanticized and personalized health recommender systems

Ertuğrul

Elçi

2019

Expert Systems

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Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre‐based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi‐dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web‐enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems.

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Irs-T2d

Cited by 19 publications

References 20 publications

Reinforcement learning and blockchain‐based intelligent and secure vaccine recommender system

Reinforcement learning and blockchain‐based intelligent and secure vaccine recommender system

A Hybrid Recommendation Model for Drug Selection

A survey on semanticized and personalized health recommender systems

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