A Semantic Web Framework to Support Knowledge Management in Chronic Disease Healthcare

Buranarach, Marut; Supnithi, Thepchai; Chalortham, Nopphadol; Khunthong, Vasuthep; Varasai, Patcharee; Kawtrakul, Asanee

doi:10.1007/978-3-642-04590-5_15

Cited by 14 publications

(10 citation statements)

References 8 publications

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“…There are numerous examples of ontology-based applications in healthcare. For example, ontologies are used in the middleware of pervasive health systems for monitoring patients and managing alerts [10] and for generating clinical reminders for clinicians [11]. Another example is TrialX, a web application that uses its own ontology to interpret and evaluate data stored in personal health records in order to match patients to clinical trials [12].…”

Section: Related Workmentioning

confidence: 99%

Generating personalized advice for schizophrenia patients

Emerencia

Krieke

Sytema

et al. 2013

Artificial Intelligence in Medicine

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Our findings suggest that an approach that uses problem severities to identify important problems for a patient corresponds closely to the way a clinician thinks. Furthermore, after applying a severity threshold, the majority of advice units selected by the system are considered relevant by the patients. Our findings pave the way for the development of systems that facilitate patient-centered care for chronic illnesses by automating the sharing of assessment results between patient and clinician.

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Section: Related Workmentioning

confidence: 99%

Generating personalized advice for schizophrenia patients

Emerencia

Krieke

Sytema

et al. 2013

Artificial Intelligence in Medicine

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“…The most relevant use of Semantic Web in DSS context is the "use ontologies and rules as a means of making DSS more intelligent" in data analysis [13]. Much of the works could be viewed as continuation of the Expert Systems family of DSS, because they are using the SW standards such as RDF or OWL as an evolution of the use of raw data into knowledge representation [14,15,16,17,18]. The use of ontologies provides both a complete specialized domain knowledge model and a set of inference rules and querying to be applied in that model.…”

Section: Dss and The Semantic Webmentioning

confidence: 99%

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

Espinoza

Fernández

Torres

2017

Collaboration in a Data-Rich World

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In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak

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“…Buranarach et.al [8] proposes a knowledge based framework for management for chronic disease care. The main idea of the proposed framework was to support decision support for clinical information component in chronic care [8]. The knowledge base for the framework is integration of ontologies, patient registry and evidence based healthcare repository.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The main focus is to make data machine readable and machine understandable. "Semantic Web" [1] technology comprises of "XML" [6],"RDF (Resource Description Framework") [7], "RDFS (Resource Description Framework Schema)" [8] and "OWL (Web Ontology Language)" [3]. "Ontologies" [3] have been recently used in information science as it supports "knowledge representation", "logic" and "inference" that is machine readable and understandable.…”

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confidence: 99%

An Ontology based Decision support for Tuberculosis Management and Control in India

Abhishek¹,

Singh²

2016

IJET

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This paper presents an ontology based formalism of existing system for TB (Tuberculosis) control and management in India that is RNTCP (Revised National TB Control Programme). India in recent years has been highest TB burden country. There are some geographical areas of India which are badly affected by TB. The formalism presented in the paper will help in further investigation /research of a new improved method in developing a decision support system for different government / NGO (Non-Government Organization) organizations.

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A Semantic Web Framework to Support Knowledge Management in Chronic Disease Healthcare

Cited by 14 publications

References 8 publications

Generating personalized advice for schizophrenia patients

Generating personalized advice for schizophrenia patients

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

An Ontology based Decision support for Tuberculosis Management and Control in India

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