{"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/bidirectional-recurrent-neural-networks-for","title":"Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records","arxiv_id":"1606.07953","date":"2016-06-25","proceeding":null,"authors":["Abhyuday Jagannatha","Hong Yu"],"abstract":"Sequence labeling for extraction of medical events and their attributes from\nunstructured text in Electronic Health Record (EHR) notes is a key step towards\nsemantic understanding of EHRs. It has important applications in health\ninformatics including pharmacovigilance and drug surveillance. The state of the\nart supervised machine learning models in this domain are based on Conditional\nRandom Fields (CRFs) with features calculated from fixed context windows. In\nthis application, we explored various recurrent neural network frameworks and\nshow that they significantly outperformed the CRF models.","url_abs":"http://arxiv.org/abs/1606.07953v2","url_pdf":"http://arxiv.org/pdf/1606.07953v2.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":"bidirectional-recurrent-neural-networks-for","repo_url":"https://github.com/abhyudaynj/birnn-bionlp","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":"event-detection","task_name":"Event Detection"},{"task_slug":"pharmacovigilance","task_name":"Pharmacovigilance"}],"methods":[{"method_slug":"crf","method_name":"CRF"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.07953","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}