{"id":166773,"date":"2013-03-01T00:00:00","date_gmt":"2013-03-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/automated-tools-for-phenotype-extraction-from-medical-records\/"},"modified":"2018-10-16T20:55:25","modified_gmt":"2018-10-17T03:55:25","slug":"automated-tools-for-phenotype-extraction-from-medical-records","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/automated-tools-for-phenotype-extraction-from-medical-records\/","title":{"rendered":"Automated Tools for Phenotype Extraction from Medical Records"},"content":{"rendered":"<p>Clinical research studying critical illness phenotypes relies on the identification of clinical syndromes defined by\u00a0consensus definitions. Historically, identifying phenotypes has required manual chart review, a time and resource\u00a0intensive process. The overall research goal of Critical Illness PHenotype ExtRaction (deCIPHER) project is to\u00a0develop automated approaches based on natural language processing and machine learning that accurately identify\u00a0phenotypes from EMR. We chose pneumonia as our first critical illness phenotype and conducted preliminary\u00a0experiments to explore the problem space. In this abstract, we outline the tools we built for processing clinical\u00a0records, present our preliminary findings for pneumonia extraction, and describe future steps.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Clinical research studying critical illness phenotypes relies on the identification of clinical syndromes defined by\u00a0consensus definitions. Historically, identifying phenotypes has required manual chart review, a time and resource\u00a0intensive process. The overall research goal of Critical Illness PHenotype ExtRaction (deCIPHER) project is to\u00a0develop automated approaches based on natural language processing and machine learning that accurately identify\u00a0phenotypes [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"American Medical Informatics Association","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the American Medical Informatics Association Clinical Research Informatics Summit (AMIA CRI'13)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the American Medical Informatics Association Clinical Research Informatics Summit (AMIA CRI'13)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Meliha Yetisgen-Yildiz, Cosmin A. 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