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Cited by 2 publications
(3 citation statements)
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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%
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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%
“…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%