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De-identifying health records by means of active learning
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2012 (engelsk)Inngår i:  , 2012Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2012.
Emneord [en]
active learning, random forests, electronic health records, clinical text mining
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-82223OAI: oai:DiVA.org:su-82223DiVA: diva2:567200
Konferanse
ICML 2012, The 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, June 26 – July 1, 2012
Merknad

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

Tilgjengelig fra: 2012-11-12 Laget: 2012-11-12 Sist oppdatert: 2013-10-01bibliografisk kontrollert

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

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