{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T03:42:53Z","timestamp":1707536573459},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children\u2019s hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task. The solution we devised will help build more reliable decision support systems.<\/jats:p>","DOI":"10.3233\/shti220079","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:31:23Z","timestamp":1654594283000},"source":"Crossref","is-referenced-by-count":1,"title":["Using Deep Learning to Improve Phenotyping from Clinical Reports"],"prefix":"10.3233","author":[{"given":"Marc","family":"Vincent","sequence":"first","affiliation":[{"name":"Institut Imagine, Paris Descartes University-Sorbonne Paris Cit\u00e9, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxime","family":"Douillet","sequence":"additional","affiliation":[{"name":"Institut Imagine, Paris Descartes University-Sorbonne Paris Cit\u00e9, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Lerner","sequence":"additional","affiliation":[{"name":"INSERM UMR1138, Centre de Recherche des Cordeliers, Team 22, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antoine","family":"Neuraz","sequence":"additional","affiliation":[{"name":"INSERM UMR1138, Centre de Recherche des Cordeliers, Team 22, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anita","family":"Burgun","sequence":"additional","affiliation":[{"name":"INSERM UMR1138, Centre de Recherche des Cordeliers, Team 22, Paris, France"},{"name":"Department of Medical Informatics, Necker-Enfants Malades Hospital, Assistance Publique \u2013 H\u00f4pitaux de Paris (AP-HP), Paris, France"},{"name":"Paris Descartes University Sorbonne Paris Cit\u00e9, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Garcelon","sequence":"additional","affiliation":[{"name":"Institut Imagine, Paris Descartes University-Sorbonne Paris Cit\u00e9, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:31:25Z","timestamp":1654594285000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220079","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}