Mining Health-related Cause-Effect Statements with High Precision at Large Scale (2022.coling-1)
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| Challenge: | Existing methods for assessing the health relatedness of phrases and sentences are slower and less effective than state-of-the-art medical entity linkers. |
| Approach: | They propose a termhood score that achieves 69% recall at over 90% precision on a web dataset with cause-effect statements. |
| Outcome: | The proposed method achieves 69% recall at over 90% precision on a web dataset with cause-effect statements. |
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| Challenge: | Exaggerations in health news can have tremendous adverse effects on the lifestyle of the common masses who feed themselves mostly on such news instead of the source scientific publication. |
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| Challenge: | Existing methods to extract semantic associations from medical literature do not take into account the semantics of sentences from which entity co-occurrences are extracted. |
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COMETA: A Corpus for Medical Entity Linking in the Social Media (2020.emnlp-main)
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| Challenge: | Existing datasets for Entity Linking (EL) fail to address the complex nature of health terminology in layman’s language. |
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| Challenge: | Existing methods for medical relation extraction use dependency syntax as a source of features. |
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| Challenge: | Information we share online unveils directly or indirectly information about our lifestyle and health situation. |
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Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines (D19-1)
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| Challenge: | Using dialog agents, we can collect family history data from in-person consultations and crowdsource it to a genetic counselor. |
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Rationalizing Medical Relation Prediction from Corpus-level Statistics (2020.acl-main)
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| Challenge: | Existing work on predicting relations based on text corpus has focused on analyzing raw texts mentioning two entities. |
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SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes (2023.findings-emnlp)
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Biomedical Concept Relatedness – A large EHR-based benchmark (2020.coling-main)
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| Challenge: | Existing biomedical concept relatedness datasets are notoriously small and consist of hand-picked concept pairs. |
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Evaluating Large Language Models for Health-related Queries with Presuppositions (2024.findings-acl)
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| Challenge: | a large number of health-related queries require factually accurate answers . however, the lack of accurate answers may cause real-world harm . |
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