{"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/medical-concept-embedding-with-time-aware","title":"Medical Concept Embedding with Time-Aware Attention","arxiv_id":"1806.02873","date":"2018-06-06","proceeding":null,"authors":["Xiangrui Cai","Jinyang Gao","Kee Yuan Ngiam","Beng Chin Ooi","Ying Zhang","Xiaojie Yuan"],"abstract":"Embeddings of medical concepts such as medication, procedure and diagnosis\ncodes in Electronic Medical Records (EMRs) are central to healthcare analytics.\nPrevious work on medical concept embedding takes medical concepts and EMRs as\nwords and documents respectively. Nevertheless, such models miss out the\ntemporal nature of EMR data. On the one hand, two consecutive medical concepts\ndo not indicate they are temporally close, but the correlations between them\ncan be revealed by the time gap. On the other hand, the temporal scopes of\nmedical concepts often vary greatly (e.g., \\textit{common cold} and\n\\textit{diabetes}). In this paper, we propose to incorporate the temporal\ninformation to embed medical codes. Based on the Continuous Bag-of-Words model,\nwe employ the attention mechanism to learn a \"soft\" time-aware context window\nfor each medical concept. Experiments on public and proprietary datasets\nthrough clustering and nearest neighbour search tasks demonstrate the\neffectiveness of our model, showing that it outperforms five state-of-the-art\nbaselines.","url_abs":"http://arxiv.org/abs/1806.02873v1","url_pdf":"http://arxiv.org/pdf/1806.02873v1.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":"medical-concept-embedding-with-time-aware","repo_url":"https://github.com/XiangruiCAI/mce","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.02873","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}