{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/characterizing-diseases-from-unstructured","title":"Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec Approach","arxiv_id":"1603.00106","date":"2016-03-01","proceeding":null,"authors":["Saurav Ghosh","Prithwish Chakraborty","Emily Cohn","John S. Brownstein","Naren Ramakrishnan"],"abstract":"Traditional disease surveillance can be augmented with a wide variety of\nreal-time sources such as, news and social media. However, these sources are in\ngeneral unstructured and, construction of surveillance tools such as\ntaxonomical correlations and trace mapping involves considerable human\nsupervision. In this paper, we motivate a disease vocabulary driven word2vec\nmodel (Dis2Vec) to model diseases and constituent attributes as word embeddings\nfrom the HealthMap news corpus. We use these word embeddings to automatically\ncreate disease taxonomies and evaluate our model against corresponding human\nannotated taxonomies. We compare our model accuracies against several\nstate-of-the art word2vec methods. Our results demonstrate that Dis2Vec\noutperforms traditional distributed vector representations in its ability to\nfaithfully capture taxonomical attributes across different class of diseases\nsuch as endemic, emerging and rare.","url_abs":"http://arxiv.org/abs/1603.00106v2","url_pdf":"http://arxiv.org/pdf/1603.00106v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"characterizing-diseases-from-unstructured","repo_url":"https://github.com/sauravcsvt/Dis2Vec_supplementary","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}