Using ontologies and probabilistic networks to develop a preventive stroke diagnosis system (PSDS)

Rodríguez‐González, Alejandro; Mayer, Miguel Ángel; Alor-Hernández, Giner; Gómez-Berbís, Juan Miguel; Cortés-Robles, Guillermo; Lemos, Angel Lagares

doi:10.1109/cbms.2010.6042672

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Ontological reasoning engines are being used to add deduction capabilities to these systems [62]. The commonest use of decision support is the diagnosis of disease decision support and it is increasingly used for detecting the risk of disease, prescribing or other intervention decisions and prevention [63][64][65].…”

Section: Decision Supportmentioning

confidence: 99%

The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review

et al. 2013

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SummaryBackground: Most chronic diseases are managed in primary and ambulatory care. The chronic care model (CCM) suggests a wide range of community, technological, team and patient factors contribute to effective chronic disease management. Ontologies have the capability to enable formalised linkage of heterogeneous data sources as might be found across the elements of the CCM. Objective: To describe the evidence base for using ontologies and other semantic integration methods to support chronic disease management. Method: We reviewed the evidence-base for the use of ontologies and other semantic integration methods within and across the elements of the CCM. We report them using a realist review describing the context in which the mechanism was applied, and any outcome measures. Results: Most evidence was descriptive with an almost complete absence of empirical research and important gaps in the evidence-base. We found some use of ontologies and semantic integration methods for community support of the medical home and for care in the community. Ubiquitous information technology (IT) and other IT tools were deployed to support self-management support, use of shared registries, health behavioural models and knowledge discovery tools to improve delivery system design. Data quality issues restricted the use of clinical data; however there was an increased use of interoperable data and health system integration. Conclusions: Ontologies and semantic integration methods are emergent with limited evidence-base for their implementation. However, they have the potential to integrate the disparate community wide data sources to provide the information necessary for effective chronic disease management.

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Section: Decision Supportmentioning

confidence: 99%

The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review

et al. 2013

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“…Following that style, the definition of Disease Y was: The goal of this paper is to give a solution to this problem because in real medical applications there are a lot of situations where a concrete disease is part of the diagnosis of another disease. The probabilistic issue of the system can be solved using previous probabi-listic modules like the once defined in ODDIN system (García-Crespo et al, 2010) or adapting newer probabilistic solutions to the actual problem (Rodríguez-González et al, 2010). The solution to this problem is able not only to solve this problem.…”

Section: The Oddin Approachmentioning

confidence: 99%

Automated Diagnosis Through Ontologies and Logical Descriptions

Colomo‐Palacios

García-Crespo

Gómez-Berbís

et al. 2011

International Journal of Decision Support System Technology

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The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

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Bidirectional General Graphs for inference. Principles and implications for medicine

Robson

2019

Computers in Biology and Medicine

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Using ontologies and probabilistic networks to develop a preventive stroke diagnosis system (PSDS)

Cited by 4 publications

References 26 publications

The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review

The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review

Automated Diagnosis Through Ontologies and Logical Descriptions

Bidirectional General Graphs for inference. Principles and implications for medicine

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