Pre-training phenotyping classifiers.
Pre-training phenotyping classifiers.
复制标题
训练前表型分类器。
DOI:
10.1016/j.jbi.2020.103626
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发表时间:
2021-01
影响因子:
4.5
通讯作者:
Miller T
中科院分区:
文献类型:
--
作者:
Dligach D;Afshar M;Miller T
Recent transformer-based pre-trained language models have become a de facto standard for many text classification tasks. Nevertheless, their utility in the clinical domain, where classification is often performed at encounter or patient level, is still uncertain due to the limitation on the maximum length of input. In this work, we introduce a self-supervised method for pre-training that relies on a masked token objective and is free from the limitation on the maximum input length. We compare the proposed method with supervised pre-training that uses billing codes as a source of supervision. We evaluate the proposed method on one publicly-available and three in-house datasets using the standard evaluation metrics such as the area under the ROC curve and F1 score. We find that, surprisingly, even though self-supervised pre-training performs slightly worse than supervised, it still preserves most of the gains from pre-training.
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DOI:
10.1136/amiajnl-2013-001935
发表时间:
2014-03
期刊:
Journal of the American Medical Informatics Association : JAMIA
影响因子:
--
作者:
Shivade C;Raghavan P;Fosler-Lussier E;Embi PJ;Elhadad N;Johnson SB;Lai AM
通讯作者:
Lai AM
DOI:
10.1136/jamia.2009.001560
发表时间:
2010-09-01
影响因子:
6.4
作者:
Savova, Guergana K.;Masanz, James J.;Chute, Christopher G.
通讯作者:
Chute, Christopher G.
影响因子:
4.5
作者:
Sushil, Madhumita;Suster, Simon;Daelemans, Walter
通讯作者:
Daelemans, Walter
影响因子:
9.8
作者:
Johnson AE;Pollard TJ;Shen L;Lehman LW;Feng M;Ghassemi M;Moody B;Szolovits P;Celi LA;Mark RG
通讯作者:
Mark RG
DOI:
10.1197/jamia.m3120
发表时间:
2009-07-01
影响因子:
6.4
作者:
Azzam, Helen C.;Khalsa, Satjeet S.;Fuchs, Barry D.
通讯作者:
Fuchs, Barry D.