A Lexical-Ontological Resource for Consumer Healthcare

Cardillo, Elena; Serafini, Luciano; Tamilin, Andrei

doi:10.1007/978-3-642-11745-9_21

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…We give more details in DEFINDER: Extracting Definitions From Consumer‐Oriented Text. Other studies have used alternative sources for consumer‐health vocabulary: (1) elicitation of terms directly from lay users to build a lexi‐ontological resource for consumer healthcare for Italian (Cardillo, ) and (2) combination of elicitation data and text from bulletin boards to collect medical facts and beliefs used to enhance WordNet with consumer health information (Smith & Fellbaum, ). Our choice of corpus is based on two desiderata: scalability and presence of definitions of consumer heath terms.…”

Section: System Architecture and Related Workmentioning

confidence: 99%

“…Studies have shown that currently there is a mismatch between general terminologies, such as WordNet (Miller, ), and technical medical terminologies, such as the UMLS (Burgun & Bodenreider, ). Recent work has been proposed to address this issue by developing methods for building consumer health vocabularies (Cardillo, ; Elhadad, ; Elhadad & Sutaria, ; Smith & Fellbaum, ; Zeng & Tony, ). One of the recent largest initiatives is the Consumer Health Vocabulary Initiative at the Harvard Medical School, which has developed the Open Source Collaborative Consumer Health Vocabulary (OSC CHV), and which aims to link lay medical terms to their corresponding technical concepts from the UMLS Metathesaurus (Keselman, Logan, Smith, Leroy, Zeng‐Treitler, Q., , Keselman, Smith, Divita, Kim, Browne, Leroy, Zeng‐Treitler, ; Zeng & Tony, ).…”

Section: Introductionmentioning

confidence: 99%

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Inducing terminologies from text: A case study for the consumer health domain

Muresan

Klavans

2013

J Am Soc Inf Sci Tec

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Specialized medical ontologies and terminologies, such as SNOMED CT and the Unified Medical Language System (UMLS), have been successfully leveraged in medical information systems to provide a standard webaccessible medium for interoperability, access, and reuse. However, these clinically oriented terminologies and ontologies cannot provide sufficient support when integrated into consumer-oriented applications, because these applications must "understand" both technical and lay vocabulary. The latter is not part of these specialized terminologies and ontologies. In this article, we propose a two-step approach for building consumer health terminologies from text: 1) automatic extraction of definitions from consumer-oriented articles and web documents, which reflects language in use, rather than relying solely on dictionaries, and 2) learning to map definitions expressed in natural language to terminological knowledge by inducing a syntactic-semantic grammar rather than using hand-written patterns or grammars. We present quantitative and qualitative evaluations of our two-step approach, which show that our framework could be used to induce consumer health terminologies from text.

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Section: System Architecture and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inducing terminologies from text: A case study for the consumer health domain

Muresan

Klavans

2013

J Am Soc Inf Sci Tec

View full text Add to dashboard Buy / Rent full text

show abstract

“…In lay terms, it is similar to concept maps or graphs but with the capacity to link with other graphs, and evoke reasoning and inferences. Some published examples and ideas include: ontology-driven clinical decision support systems for patients in regards to discharge medication [ 8 ], building medical ontology models for Italian patients [ 9 ], an ontology-based coaching tool for physicians to prepare dialogue with patients [ 10 ], and dialogue systems for patient planning [ 11 ] …”

Section: Introductionmentioning

confidence: 99%

Developing VISO: Vaccine Information Statement Ontology for patient education

et al. 2015

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ObjectiveTo construct a comprehensive vaccine information ontology that can support personal health information applications using patient-consumer lexicon, and lead to outcomes that can improve patient education.MethodsThe authors composed the Vaccine Information Statement Ontology (VISO) using the web ontology language (OWL). We started with 6 Vaccine Information Statement (VIS) documents collected from the Centers for Disease Control and Prevention (CDC) website. Important and relevant selections from the documents were recorded, and knowledge triples were derived. Based on the collection of knowledge triples, the meta-level formalization of the vaccine information domain was developed. Relevant instances and their relationships were created to represent vaccine domain knowledgeResultsThe initial iteration of the VISO was realized, based on the 6 Vaccine Information Statements and coded into OWL2 with Protégé. The ontology consisted of 132 concepts (classes and subclasses) with 33 types of relationships between the concepts. The total number of instances from classes totaled at 460, along with 429 knowledge triples in total. Semiotic-based metric scoring was applied to evaluate quality of the ontology.

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A Lexical-Ontological Resource for Consumer Healthcare

Cited by 2 publications

References 5 publications

Inducing terminologies from text: A case study for the consumer health domain

Inducing terminologies from text: A case study for the consumer health domain

Developing VISO: Vaccine Information Statement Ontology for patient education

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