| DOI | Trouver le DOI : https://doi.org/10.1007/978-3-642-02976-9_46 |
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| Auteur | Rechercher : Wang, Yunli1; Rechercher : Liu, Hongyu1; Rechercher : Geng, Liqiang1; Rechercher : Keays, Matthew S.1; Rechercher : You, Yonghua1 |
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| Affiliation | - Conseil national de recherches du Canada. Institut de technologie de l'information du CNRC
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| Format | Texte, Chapitre de livre |
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| Conférence | 12th Conference on Artificial Intelligence in Medicine (AIME 2009), July 18-22, 2009, Verona, Italy |
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| Résumé | With the increasing usage of computers and Internet, personal health information (PHI) is distributed across multiple institutes and often scattered on multiple devices and stored in diverse formats. Non-traditional medical records such as emails and e-documents containing PHI are in a high risk of privacy leakage. We are facing the challenges of locating and managing PHI in the distributed environment. The goal of this study is to classify electronic documents into PHI and non-PHI. A supervised machine learning method was used for this text categorization task. Three classifiers: SVM, decision tree and Naive Bayesian were used and tested on three data sets. Lexical, semantic and syntactic features and their combinations were compared in terms of their effectiveness of classifying PHI documents. The results show that combining semantic and/or syntactic with lexical features is more effective than lexical features alone for PHI classification. The supervised machine learning method is effective in classifying documents into PHI and non-PHI. |
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| Date de publication | 2009-08 |
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| Maison d’édition | Springer Berlin Heidelberg |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro NPARC | 19291881 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 41312d40-70a5-4b35-b3b1-9ecf45abf14a |
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| Enregistrement créé | 2012-01-24 |
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| Enregistrement modifié | 2020-06-17 |
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