{"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-patient-representations-from-text","title":"Learning Patient Representations from Text","arxiv_id":"1805.02096","date":"2018-05-05","proceeding":"SEMEVAL 2018 6","authors":["Dmitriy Dligach","Timothy Miller"],"abstract":"Mining electronic health records for patients who satisfy a set of predefined\ncriteria is known in medical informatics as phenotyping. Phenotyping has\nnumerous applications such as outcome prediction, clinical trial recruitment,\nand retrospective studies. Supervised machine learning for phenotyping\ntypically relies on sparse patient representations such as bag-of-words. We\nconsider an alternative that involves learning patient representations. We\ndevelop a neural network model for learning patient representations and show\nthat the learned representations are general enough to obtain state-of-the-art\nperformance on a standard comorbidity detection task.","url_abs":"http://arxiv.org/abs/1805.02096v1","url_pdf":"http://arxiv.org/pdf/1805.02096v1.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-patient-representations-from-text","repo_url":"https://github.com/dmitriydligach/starsem2018-patient-representations","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"}],"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}