Abstract：
OBJECTIVE:To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. MATERIALS AND METHODS:There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. RESULTS:When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. DISCUSSION:The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. CONCLUSION:Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations.
journal_name
Int J Med Informjournal_title
International journal of medical informaticsauthors
Kholghi M,Sitbon L,Zuccon G,Nguyen Adoi
10.1016/j.ijmedinf.2017.08.001subject
Has Abstractpub_date
2017-10-01 00:00:00pages
25-31eissn
1386-5056issn
1872-8243pii
S1386-5056(17)30200-9journal_volume
106pub_type
杂志文章abstract：BACKGROUND:Medication errors in hospital discharge summaries have the potential to cause serious harm to patients. These errors are generally associated with manual transcription of medications between medication charts and discharge summaries. Studies also show junior doctors are more likely to contribute to discharge...
journal_title：International journal of medical informatics
pub_type： 杂志文章
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journal_title：International journal of medical informatics
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journal_title：International journal of medical informatics
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journal_title：International journal of medical informatics
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pub_type： 杂志文章
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pub_type： 杂志文章,评审
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更新日期：2010-11-01 00:00:00
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journal_title：International journal of medical informatics
pub_type： 杂志文章
doi：10.1016/s1386-5056(97)00020-8
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pub_type： 杂志文章,评审
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pub_type： 杂志文章
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更新日期：2011-12-01 00:00:00
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pub_type： 杂志文章
doi：10.1016/j.ijmedinf.2008.07.016
更新日期：2009-04-01 00:00:00
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journal_title：International journal of medical informatics
pub_type： 杂志文章
doi：10.1016/j.ijmedinf.2008.10.003
更新日期：2009-04-01 00:00:00
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更新日期：2016-02-01 00:00:00
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更新日期：2019-09-01 00:00:00
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doi：10.1016/j.ijmedinf.2014.01.001
更新日期：2014-05-01 00:00:00
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pub_type： 杂志文章
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doi：10.1016/j.ijmedinf.2020.104365
更新日期：2021-03-01 00:00:00
