Temporal information extraction from clinical text

In this paper, we present a method for temporal relation extraction from clinical narratives in French and in English. We experiment on two comparable corpora, the MERLOT corpus and the THYME corpus, and show that a common approach can be used for both languages.

[1]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[2]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[3]  Pierre Zweigenbaum,et al.  Annotation of specialized corpora using a comprehensive entity and relation scheme , 2014, LREC.

[4]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[5]  Yaoyun Zhang,et al.  UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes , 2016, *SEMEVAL.

[6]  Tommaso Caselli,et al.  VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval , 2016, SemEval@NAACL-HLT.

[7]  David K. Vawdrey,et al.  HARVEST, a longitudinal patient record summarizer , 2014, J. Am. Medical Informatics Assoc..

[8]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[9]  M. Saeed.  Multiparameter Intelligent Monitoring in Intensive Care II ( MIMIC-II ) : A public-access intensive care unit database , 2011 .

[10]  Petr Sojka,et al.  Software Framework for Topic Modelling with Large Corpora , 2010 .

[11]  Maria Leonor Pacheco,et al.  of the Association for Computational Linguistics: , 2001 .

[12]  James Pustejovsky,et al.  SemEval-2017 Task 12: Clinical TempEval , 2017, *SEMEVAL.

[13]  Stéphane M. Meystre,et al.  UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text , 2016, *SEMEVAL.

[14]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[15]  Veera Raghavendra Chikka.  CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques , 2016, *SEMEVAL.

[16]  James Pustejovsky,et al.  SemEval-2015 Task 6: Clinical TempEval , 2015, *SEMEVAL.

[17]  Olivier Ferret,et al.  LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers , 2016, *SEMEVAL.

[18]  Anna Rumshisky,et al.  Evaluating temporal relations in clinical text: 2012 i2b2 Challenge , 2013, J. Am. Medical Informatics Assoc..

[19]  Yi Yang,et al.  Unsupervised Multi-Domain Adaptation with Feature Embeddings , 2015, NAACL.

[20]  David D. Cox,et al.  Making a Science of Model Search , 2012, ArXiv.

[21]  Sunghwan Sohn,et al.  Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications , 2010, J. Am. Medical Informatics Assoc..

[22]  James Pustejovsky,et al.  Increasing Informativeness in Temporal Annotation , 2011, Linguistic Annotation Workshop.

[23]  David D. Cox,et al.  Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures , 2013, ICML.

[24]  Nazli Goharian,et al.  GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives , 2016, SemEval@NAACL-HLT.

[25]  Chen Lin,et al.  Temporal Annotation in the Clinical Domain , 2014, TACL.

[26]  Cyril Grouin,et al.  De-identification of clinical notes in French: towards a protocol for reference corpus development , 2014, J. Biomed. Informatics.