{"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/learning-contextual-hierarchical-structure-of","title":"Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes","arxiv_id":"1811.01294","date":"2018-11-03","proceeding":null,"authors":["Brett K. Beaulieu-Jones","Isaac S. Kohane","Andrew L. Beam"],"abstract":"Biomedical association studies are increasingly done using clinical concepts,\nand in particular diagnostic codes from clinical data repositories as\nphenotypes. Clinical concepts can be represented in a meaningful, vector space\nusing word embedding models. These embeddings allow for comparison between\nclinical concepts or for straightforward input to machine learning models.\nUsing traditional approaches, good representations require high dimensionality,\nmaking downstream tasks such as visualization more difficult. We applied\nPoincar\\'e embeddings in a 2-dimensional hyperbolic space to a large-scale\nadministrative claims database and show performance comparable to\n100-dimensional embeddings in a euclidean space. We then examine disease\nrelationships under different disease contexts to better understand potential\nphenotypes.","url_abs":"http://arxiv.org/abs/1811.01294v1","url_pdf":"http://arxiv.org/pdf/1811.01294v1.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":"learning-contextual-hierarchical-structure-of","repo_url":"https://github.com/brettbj/poincareembeddings","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"diagnostic","task_name":"Diagnostic"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}