{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T23:06:01Z","timestamp":1776294361325,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685335","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":[[2024,8,22]]},"abstract":"<jats:p>Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) presents significant privacy and security challenges. This study explores the application of Federated Learning (FL) to overcome these challenges within the context of French EHRs. By utilizing a multilingual BERT model in an FL simulation involving 20 hospitals, each represented by a unique medical department or pole, we compared the performance of two setups: individual models, where each hospital uses only its own training and validation data without engaging in the FL process, and federated models, where multiple hospitals collaborate to train a global FL model. Our findings demonstrate that FL models not only preserve data confidentiality but also outperform the individual models. In fact, the Global FL model achieved an F1 score of 75,7%, slightly comparable to that of the Centralized approach at 78,5%. This research underscores the potential of FL in extracting PIIs from EHRs, encouraging its broader adoption in health data analysis.<\/jats:p>","DOI":"10.3233\/shti240488","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:26:06Z","timestamp":1724405166000},"source":"Crossref","is-referenced-by-count":5,"title":["Secure Extraction of Personal Information from EHR by Federated Machine Learning"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4741-9820","authenticated-orcid":false,"given":"Mohamed","family":"El Azzouzi","sequence":"first","affiliation":[{"name":"Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, F-35000, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reda","family":"Bellafqira","sequence":"additional","affiliation":[{"name":"IMT Atlantique, INSERM, LATIM-UMR 1101, F-29200, Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gouenou","family":"Coatrieux","sequence":"additional","affiliation":[{"name":"IMT Atlantique, INSERM, LATIM-UMR 1101, F-29200, Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Cuggia","sequence":"additional","affiliation":[{"name":"Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, F-35000, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillaume","family":"Bouzille","sequence":"additional","affiliation":[{"name":"Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, F-35000, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240488","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:26:07Z","timestamp":1724405167000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240488"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240488","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}