SemMed: Applying Semantic Web to Medical Recommendation Systems

Rodríguez, Alejandro; Jiménez, Enrique; Fernández, José‐Jesús; Eccius, Martin; Gómez, Juan Miguel; Alor-Hernández, Giner; Posada-Gómez, Rubén; Laufer, Carlos

doi:10.1109/intensive.2009.12

Cited by 18 publications

(17 citation statements)

References 7 publications

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“…Semantic network and ontologies have been utilized by researchers for analyzing medical data in the past [[24], [25], [26], [27], [28]]. The popularity of semantic network and ontologies in the field of medicine is mainly due to their power in representing data and the relationship among the dataset elements.…”

Section: Discussionmentioning

confidence: 99%

A rule-based semantic approach for data integration, standardization and dimensionality reduction utilizing the UMLS: Application to predicting bariatric surgery outcomes

Modaresnezhad

Vahdati

Nemati

et al. 2019

Computers in Biology and Medicine

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Section: Discussionmentioning

confidence: 99%

A rule-based semantic approach for data integration, standardization and dimensionality reduction utilizing the UMLS: Application to predicting bariatric surgery outcomes

Modaresnezhad

Vahdati

Nemati

et al. 2019

Computers in Biology and Medicine

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“…SemMed [14] which is a medical recommendation engine based on Semantic Web Technologies, applies an ontologybased approach. It consists of an inference engine, a rules manager, a support database, and ontology manager.…”

Section: ) Ontology and Rule-based Medicine Recommendation Systemsmentioning

confidence: 99%

A Literature Review on Medicine Recommender Systems

Stark¹,

Knahl²,

Aydın³

et al. 2019

IJACSA

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Medicine recommender systems can assist the medical care providers with the selection of an appropriate medication for the patients. The advanced technologies available nowadays can help developing such recommendation systems which can lead to more concise decisions. Many existing medicine recommendation systems are developed based on different algorithms. Thus, it is crucial to understand the state-of-the-art developments of these systems, their advantages and disadvantages as well as areas which require more research. In this paper, we conduct a literature review on the existing solutions for medicine recommender systems, describe and compare them based on various features, and present future research directions.

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“…The semantic models for RS are fast moving in a multidisciplinary way. For example, customer relationship management [41], tourism [42], multimedia [43], travel [37], health domain [44] or [45], games [26], financial [46], social networks [47], e-commerce [48] or media [49] to cite just some of the most relevant cases.…”

Section: Semantic Models For Recommendationmentioning

confidence: 99%

SMORE: Towards a semantic modeling for knowledge representation on social media

Villanueva

González‐Carrasco

López-Cuadrado

et al. 2016

Science of Computer Programming

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This research presents SMORE, a semantic model for knowledge representation on social media. In order to provide recommendations, the model provides the elements for representing the content through the use of an ontological model and semantic techniques for the characterization and relationships between user profiles, products and social networks. In fact, with this model could be the basis of recommendation system based on social media data, and it could be exploited use by the recommendations on different products, which are stored in the Web and with similar characteristics between them. Moreover, SMORE represent the information related to user of the social networks in order to have a user characterization to be used for future recommendations in several domains. The semantic model has been evaluated with semantic data extracted from a trusted social network as, Twitter, obtaining the information specified by an expert in the field of marketing for recommendations in the automotive industry.

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SemMed: Applying Semantic Web to Medical Recommendation Systems

Cited by 18 publications

References 7 publications

A rule-based semantic approach for data integration, standardization and dimensionality reduction utilizing the UMLS: Application to predicting bariatric surgery outcomes

A rule-based semantic approach for data integration, standardization and dimensionality reduction utilizing the UMLS: Application to predicting bariatric surgery outcomes

A Literature Review on Medicine Recommender Systems

SMORE: Towards a semantic modeling for knowledge representation on social media

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