Automatic de-identification of French clinical records: comparison of rule-based and machine-learning approaches.
Identifieur interne : 000172 ( Main/Exploration ); précédent : 000171; suivant : 000173Automatic de-identification of French clinical records: comparison of rule-based and machine-learning approaches.
Auteurs : Cyril Grouin [France] ; Pierre ZweigenbaumSource :
- Studies in health technology and informatics [ 0926-9630 ] ; 2013.
Descripteurs français
- Wicri :
- geographic : France.
English descriptors
- KwdEn :
- MESH :
Abstract
In this paper, we present a comparison of two approaches to automatically de-identify medical records written in French: a rule-based system and a machine-learning based system using a conditional random fields (CRF) formalism. Both systems have been designed to process nine identifiers in a corpus of medical records in cardiology. We performed two evaluations: first, on 62 documents in cardiology, and on 10 documents in foetopathology - produced by optical character recognition (OCR) - to evaluate the robustness of our systems. We achieved a 0.843 (rule-based) and 0.883 (machine-learning) exact match overall F-measure in cardiology. While the rule-based system allowed us to achieve good results on nominative (first and last names) and numerical data (dates, phone numbers, and zip codes), the machine-learning approach performed best on more complex categories (postal addresses, hospital names, medical devices, and towns). On the foetopathology corpus, although our systems have not been designed for this corpus and despite OCR character recognition errors, we obtained promising results: a 0.681 (rule-based) and 0.638 (machine-learning) exact-match overall F-measure. This demonstrates that existing tools can be applied to process new documents of lower quality.
PubMed: 23920600
Affiliations:
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Le document en format XML
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Both systems have been designed to process nine identifiers in a corpus of medical records in cardiology. We performed two evaluations: first, on 62 documents in cardiology, and on 10 documents in foetopathology - produced by optical character recognition (OCR) - to evaluate the robustness of our systems. We achieved a 0.843 (rule-based) and 0.883 (machine-learning) exact match overall F-measure in cardiology. While the rule-based system allowed us to achieve good results on nominative (first and last names) and numerical data (dates, phone numbers, and zip codes), the machine-learning approach performed best on more complex categories (postal addresses, hospital names, medical devices, and towns). On the foetopathology corpus, although our systems have not been designed for this corpus and despite OCR character recognition errors, we obtained promising results: a 0.681 (rule-based) and 0.638 (machine-learning) exact-match overall F-measure. This demonstrates that existing tools can be applied to process new documents of lower quality.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Île-de-France</li></region><settlement><li>Orsay</li></settlement></list><tree><noCountry><name sortKey="Zweigenbaum, Pierre" sort="Zweigenbaum, Pierre" uniqKey="Zweigenbaum P" first="Pierre" last="Zweigenbaum">Pierre Zweigenbaum</name></noCountry><country name="France"><region name="Île-de-France"><name sortKey="Grouin, Cyril" sort="Grouin, Cyril" uniqKey="Grouin C" first="Cyril" last="Grouin">Cyril Grouin</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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