Context-Awareness Based Personalized Recommendation of Anti-Hypertension Drugs

Chen, Dexin; Jin, Dawei; Goh, Tiong‐Thye; Li, Na; Wei, Leiru

doi:10.1007/s10916-016-0560-z

Cited by 27 publications

(24 citation statements)

References 22 publications

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“…Most of these systems are put forward to accomplish a specific task, such as treatment or diagnosis tasks. None of them has defined the diagnostic process, compared to Chen, Jin, et al (2016) who focused on a specific application (Hypertension disease). It is a context‐aware system that treats diverse contexts by taking into account patients' states and medical devices. It offers user‐friendly interfaces that can be employed by three different users (patients, doctors, and administrators). However, the majority of other systems have been proposed for just one user; patients. Our system is flexible as it presents a graphical interface to doctors to help them add or delete new patients and medical objects in the knowledge base. It provides patients with other various functions, such as the possibility to contact their doctors via a graphical interface which contains their healthcare information.…”

Section: Discussionmentioning

confidence: 99%

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A semantic‐enabled and context‐aware monitoring system for the internet of medical things

et al. 2020

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The emergence of the Internet of Things (IoT) in the medical field has led to the massive deployment of a myriad of medical connected objects (MCOs). These MCOs are being developed and implemented for remote healthcare monitoring purposes including elderly patients with chronic diseases, pregnant women, and patients with disabilities. Accordingly, different associated challenges are emerging and include the heterogeneity of the gathered health data from these MCOs with ever‐changing contexts. These contexts are relative to the continuous change of constraints and requirements of the MCOs deployment (time, location, state). Other contexts are related to the patient (medical record, state, age, sex, etc.) that should be taken into account to ensure a more precise and appropriate treatment of the patient. These challenges are difficult to address due to the absence of a reference model for describing the health data and their sources and linking these data with their contexts. This article addresses this problem and introduces a semantic‐based context‐aware system (IoT Medicare system) for patient monitoring with MCOs. This system is based on a core domain ontology (HealthIoT‐O), that is, designed to describe the semantic of heterogeneous MCOs and their data. Moreover, an efficient interpretation and management of this knowledge in diverse contexts are ensured through SWRL rules such as the verification of the proper functioning of the MCOs and the analysis of the health data for diagnosis and treatment purposes. A case study of gestational diabetes disease management is proposed to evaluate the effectiveness of the implemented IoT Medicare system. An evaluation phase is provided and focuses on the quality of the elaborated semantic model and the performance of the system.

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Section: Discussionmentioning

confidence: 99%

“…The work achieved by Chen, Jin, Goh, Li, and Wei (2016a), the authors proposed an anti‐hypertensive Drugs Personalized Recommendation Service Context Ontology (HyRCO). This model is divided into seven core classes, namely: User , Activity , Environment , Device , Service , Location and Anti‐hypertensive .…”

Section: Literature Reviewmentioning

confidence: 99%

A semantic‐enabled and context‐aware monitoring system for the internet of medical things

et al. 2020

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“…A RS of anti-hypertensive drugs based on context-awareness and designed a context ontology was proposed by Chen et al 27 Their system is capable of real-time sensing the users' context with wearable and medical sensor devices and provides reliable anti-hypertensive drug recommendations that fulfill users' need for drug information. They used SW and ontology engineering technologies to analyze user's preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

A rule-based decision support system for aiding iron deficiency management

Ertuğrul

Toygar

Foroutan

2021

Health Informatics J

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Iron is a vital mineral for the proper function of hemoglobin which is also a protein needed to transport oxygen in the blood. The lack of iron in human blood causes a range of serious health problems including “anemia.” In this article, the COntAneRS (Clinical ONTology-based Iron Deficiency‐ANEmia‐ Recommendation System) is proposed as a clinical decision support system to diagnose iron deficiency and manage its treatment. The applied methodologies and main technical contributions of this study are discussed in four aspects: (1) Iron Deficiency Domain Ontology (IDDOnt), (2) Semantic Web Rule Knowledgebase (SWRL), (3) Inference Engine, and (4) Physician Portal of the system. Experimental studies of the proposed system have been applied on a population of 200 people, consisting of real anemia patients and healthy individuals. First, a decision tree classifier is used to diagnose iron deficiency condition based on the patients’ demographic information and certain medical data, as well as recently measured hemoglobin CBC levels of the patients. To check the effectiveness of the system, the data of 50 anonymous patients randomly selected from 200 patients are entered manually in the IDDOnt and the system is then verified according to the inferencing results. After inferencing step, the recommendations related to appropriate iron drugs, daily consumption dose, drug consumption periods, and additional medical suggestions about drug interactions are provided by the system to the responsible physician through system ontology, SWRL rules, and web services. As a result of experimental studies, our system has provided very good accuracy (99.5%) and robust results in producing patient-suitable suggestions. In addition, the applicability of the system on the cases is discussed as case studies in this paper. The results reported from the applied case studies are promising in demonstrating the applicability, effectiveness, and efficiency of the proposed approach.

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“…Chen, Jin, Goh, Li, and Wei () proposed a personalized RS of antihypertensive drugs based on context‐awareness and own‐design context ontology. Their system is capable of real‐time sensing users' context with wearable and medical sensor devices and provides reliable antihypertensive drug recommendation.…”

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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Context-Awareness Based Personalized Recommendation of Anti-Hypertension Drugs

Cited by 27 publications

References 22 publications

A semantic‐enabled and context‐aware monitoring system for the internet of medical things

A semantic‐enabled and context‐aware monitoring system for the internet of medical things

A rule-based decision support system for aiding iron deficiency management

A survey on semanticized and personalized health recommender systems

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