Transfer Learning for the Prediction of Entity Modifiers in Clinical Text: Application to Opioid Use Disorder Case Detection
CoRR(2024)
摘要
Background: The semantics of entities extracted from a clinical text can be
dramatically altered by modifiers, including entity negation, uncertainty,
conditionality, severity, and subject. Existing models for determining
modifiers of clinical entities involve regular expression or features weights
that are trained independently for each modifier.
Methods: We develop and evaluate a multi-task transformer architecture design
where modifiers are learned and predicted jointly using the publicly available
SemEval 2015 Task 14 corpus and a new Opioid Use Disorder (OUD) data set that
contains modifiers shared with SemEval as well as novel modifiers specific for
OUD. We evaluate the effectiveness of our multi-task learning approach versus
previously published systems and assess the feasibility of transfer learning
for clinical entity modifiers when only a portion of clinical modifiers are
shared.
Results: Our approach achieved state-of-the-art results on the ShARe corpus
from SemEval 2015 Task 14, showing an increase of 1.1
1.7
Conclusions: We show that learned weights from our shared model can be
effectively transferred to a new partially matched data set, validating the use
of transfer learning for clinical text modifiers
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