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De-identifying health records by means of active learning
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2012 (English)In:  , 2012Conference paper, Published paper (Refereed)
Abstract [en]

An experiment on classifying words in Swedish health records as belonging to one of eight protected health information (PHI) classes, or to the non-PHI class, by means of active learning has been conducted, in which three selection strategies were evaluated in conjunction with random forests; the commonly employed approach of choosing the most uncertain examples, choosing randomly, and choosing the most certain examples. Surprisingly, random selection outperformed choosing the most uncertain examples with respect to ten considered performance metrics. Moreover, choosing the most certain examples outperformed random selection with respect to nine out of ten metrics.

Place, publisher, year, edition, pages
2012.
Keyword [en]
active learning, random forests, electronic health records, clinical text mining
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-82223OAI: oai:DiVA.org:su-82223DiVA: diva2:567200
Conference
ICML 2012, The 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, June 26 – July 1, 2012
Note

The paper was presented at the ICML Workshop on Machine Learning for Clinical Data Analysis.

Available from: 2012-11-12 Created: 2012-11-12 Last updated: 2013-10-01Bibliographically approved

Open Access in DiVA

No full text

Other links

http://people.cs.pitt.edu/~milos/icml_clinicaldata_2012/Papers/Poster_Bostrom_elal_ICML_Clinical_2012.pdf

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CiteExportLink to record
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Citation style
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