{"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/a-novel-framework-to-expedite-systematic","title":"A Novel Framework to Expedite Systematic Reviews by Automatically Building Information Extraction Training Corpora","arxiv_id":"1606.06424","date":"2016-06-21","proceeding":null,"authors":["Tanmay Basu","Shraman Kumar","Abhishek Kalyan","Priyanka Jayaswal","Pawan Goyal","Stephen Pettifer","Siddhartha R. Jonnalagadda"],"abstract":"A systematic review identifies and collates various clinical studies and\ncompares data elements and results in order to provide an evidence based answer\nfor a particular clinical question. The process is manual and involves lot of\ntime. A tool to automate this process is lacking. The aim of this work is to\ndevelop a framework using natural language processing and machine learning to\nbuild information extraction algorithms to identify data elements in a new\nprimary publication, without having to go through the expensive task of manual\nannotation to build gold standards for each data element type. The system is\ndeveloped in two stages. Initially, it uses information contained in existing\nsystematic reviews to identify the sentences from the PDF files of the included\nreferences that contain specific data elements of interest using a modified\nJaccard similarity measure. These sentences have been treated as labeled data.A\nSupport Vector Machine (SVM) classifier is trained on this labeled data to\nextract data elements of interests from a new article. We conducted experiments\non Cochrane Database systematic reviews related to congestive heart failure\nusing inclusion criteria as an example data element. The empirical results show\nthat the proposed system automatically identifies sentences containing the data\nelement of interest with a high recall (93.75%) and reasonable precision\n(27.05% - which means the reviewers have to read only 3.7 sentences on\naverage). The empirical results suggest that the tool is retrieving valuable\ninformation from the reference articles, even when it is time-consuming to\nidentify them manually. Thus we hope that the tool will be useful for automatic\ndata extraction from biomedical research publications. The future scope of this\nwork is to generalize this information framework for all types of systematic\nreviews.","url_abs":"http://arxiv.org/abs/1606.06424v1","url_pdf":"http://arxiv.org/pdf/1606.06424v1.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":"a-novel-framework-to-expedite-systematic","repo_url":"https://github.com/tanmaybasu/Data-Elements-Extrcation-from-Literature-Using-NLP-and-Machine-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-novel-framework-to-expedite-systematic","repo_url":"https://github.com/tanmaybasu/Data-Elements-Extrcation-of-Congestive-Heart-Failure-from-Relevant-Literature","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-novel-framework-to-expedite-systematic","repo_url":"https://github.com/tanmaybasu/Towards-Expediting-the-Process-of-Building-Systematic-Review-using-Machine-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}