Yearb Med Inform 2016; 25(01): 234-239
DOI: 10.15265/IY-2016-049
IMIA and Schattauer GmbH
Georg Thieme Verlag KG Stuttgart

Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest

A. Névéol
1   LIMSI CNRS UPR 3251, Université Paris Saclay, Orsay, France
,
P. Zweigenbaum
1   LIMSI CNRS UPR 3251, Université Paris Saclay, Orsay, France
,
Section Editors for the IMIA Yearbook Section on Natural Language Processing › Author Affiliations
Further Information

Correspondence to:

Aurélie Névéol, Pierre Zweigenbaum
LIMSI CNRS UPR 3251
Université Paris Saclay
Rue John von Neumann
91400 Orsay
France

Publication History

10 November 2016

Publication Date:
06 March 2018 (online)

 

Summary

Objective: To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP).

Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers.

Results: The clinical NLP best paper selection shows that clinical NLP is making use of a variety of texts of clinical interest to contribute to the analysis of clinical information and the building of a body of clinical knowledge. The full review process highlighted five papers analyzing patient-authored texts or seeking to connect and aggregate multiple sources of information. They provide a contribution to the development of methods, resources, applications, and sometimes a combination of these aspects.

Conclusions: The field of clinical NLP continues to thrive through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques to impact clinical practice. Foundational progress in the field makes it possible to leverage a larger variety of texts of clinical interest for healthcare purposes.


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  • References

  • 1 Demner-Fushman D, Elhadad N. Aspiring to unintended consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.. Yearb Med Inform 2016
  • 2 Li Q, Spooner SA, Kaiser M, Lingren N, Robbins J, Lingren T. et al. An end-to-end hybrid algorithm for automated medication discrepancy detection.. BMC Med Inform Decis Mak 2015; May 6 15: 37.
  • 3 Luo Y, Xin Y, Hochberg E, Joshi R, Uzuner O, Szolovits P. Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 1009-19.
  • 4 Ni Y, Kennebeck S, Dexheimer JW, McAneney CM, Tang H, Lingren T. et al. Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.. J Am Med Inform Assoc 2015; Jan 22 (Suppl. 01) 166-78.
  • 5 Oronoz M, Gojenola K, Pérez A, de Ilarraza AD, Casillas A. On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions.. J Biomed Inform 2015; Aug 56: 318-32.
  • 6 Prud’hommeaux E, Roark B. Graph-Based Word Alignment for Clinical Language Evaluation.. Computational Linguistics 2015; Dec 41 4: 549-78.
  • 7 Journal of Biomedical Informatics.. Volume 58, Supplement, December 2015, Proceedings of the 2014 i2b2/UTHealth Shared-Tasks and Workshop on Challenges in Natural Language Processing for Clinical Data.
  • 8 Pradhan S, Elhadad N, South BR, Martinez D, Christensen L, Vogel A. et al. Evaluating the state of the art in disorder recognition and normalization of the clinical narrative.. J Am Med Inform Assoc 2015; Jan 22 (Suppl. 01) 143-54.
  • 9 Lin C, Dligach D, Miller TA, Bethard S, Savova GK. Multilayered temporal modeling for the clinical domain.. J Am Med Inform Assoc 2016; Mar 23 (Suppl. 02) 387-95.
  • 10 Chapman WW, Nadkarni PM, Hirschman L, D’Avolio LW, Savova GK, Uzuner O. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions.. J Am Med Inform Assoc 2011; SepOct 18 (Suppl. 05) 540-3.
  • 11 Zheng K, Vydiswaran VG, Liu Y, Wang Y, Stubbs A, Uzuner Ö. et al. Ease of adoption of clinical natural language processing software: An evaluation of five systems.. J Biomed Inform 2015; Dec 58 Suppl: S189-96.
  • 12 Suominen H, Zhou L, Hanlen L, Ferraro G. Benchmarking clinical speech recognition and information extraction: new data, methods, and evaluations.. JMIR Med Inform 2015; Apr 27 3 (Suppl. 02) e19.
  • 13 Xiao B, Imel ZE, Georgiou PG, Atkins DC, Narayanan SS. “Rate My Therapist”: Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing.. PLoS One 2015; Dec 2 10 (Suppl. 012) e0143055.
  • 14 Liu X, Chen H. A research framework for pharmacovigilance in health social media: Identification and evaluation of patient adverse drug event reports.. J Biomed Inform 2015; Dec 58: 268-79.
  • 15 Nikfarjam A, Sarker A, O’Connor K, Ginn R, Gonzalez G. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.. J Am Med Inform Assoc 2015; May 22 (Suppl. 03) 671-81.
  • 16 Yu S, Liao KP, Shaw SY, Gainer VS, Churchill SE, Szolovits P. et al. Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 993-1000.
  • 17 Mehrabi S, Krishnan A, Sohn S, Roch AM, Schmidt H, Kesterson J. et al. DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx.. J Biomed Inform 2015; Apr 54: 213-9.
  • 18 Wu S, Miller T, Masanz J, Coarr M, Halgrim S, Carrell D. et al. Negation’s not solved: generalizability versus optimizability in clinical natural language processing.. PLoS One 2014; Nov 13 9 (Suppl. 11) e112774.
  • 19 Jiang M, Huang Y, Fan JW, Tang B, Denny J, Xu H. Parsing clinical text: how good are the state-of-the-art parsers?. BMC Med Inform Decis Mak 2015; 15 (Suppl. 01) S2.
  • 20 Wang Y, Pakhomov S, Ryan JO, Melton GB. Domain adaption of parsing for operative notes.. J Biomed Inform 2015; Apr 54: 1-9.
  • 21 Zhang Y, Tang B, Jiang M, Wang J, Xu H. Domain adaptation for semantic role labeling of clinical text.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 967-79.
  • 22 Moon S, McInnes B, Melton GB. Challenges and practical approaches with word sense disambiguation of acronyms and abbreviations in the clinical domain.. Healthc Inform Res 2015; Jan 21 (Suppl. 01) 35-42.
  • 23 Wu Y, Denny JC, Rosenbloom ST, Miller RA, Giuse DA, Song M. et al. A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time.. Appl Clin Inform 2015; Jun 3 6 (Suppl. 02) 364-74.
  • 24 Sun W, Rumshisky A, Uzuner O. Normalization of relative and incomplete temporal expressions in clinical narratives.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 1001-8.
  • 25 Lin C, Karlson EW, Dligach D, Ramirez MP, Miller TA, Mo H. et al. Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record.. J Am Med Inform Assoc 2015; Apr 22 (Suppl. 01) e151-61.
  • 26 McCoy TH, Castro VM, Cagan A, Roberson AM, Kohane IS, Perlis RH. Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study.. PLoS One 2015; Aug 24 10 (Suppl. 08) e0136341.
  • 27 Meng F, Morioka C. Automating the generation of lexical patterns for processing free text in clinical documents.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 980-6.
  • 28 Shivade C, Hebert C, Lopetegui M, de Marneffe MC, Fosler-Lussier E, Lai AM. Textual inference for eligibility criteria resolution in clinical trials.. J Biomed Inform 2015; Dec 58 Suppl: S211-8.
  • 29 Koopman B, Zuccon G, Nguyen A, Bergheim A, Grayson N. Automatic ICD-10 classification of cancers from free-text death certificates.. Int J Med Inform 2015; Nov 84 (Suppl. 011) 956-65.
  • 30 McCoy TH, Castro VM, Rosenfield HR, Cagan A, Kohane IS, Perlis RH. A clinical perspective on the relevance of research domain criteria in electronic health records.. Am J Psychiatry 2015; Apr 172 (Suppl. 04) 316-20.
  • 31 Joffe E, Pettigrew EJ, Herskovic JR, Bearden CF, Bernstam EV. Expert guided natural language processing using one-class classification.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 962-6.

Correspondence to:

Aurélie Névéol, Pierre Zweigenbaum
LIMSI CNRS UPR 3251
Université Paris Saclay
Rue John von Neumann
91400 Orsay
France

  • References

  • 1 Demner-Fushman D, Elhadad N. Aspiring to unintended consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.. Yearb Med Inform 2016
  • 2 Li Q, Spooner SA, Kaiser M, Lingren N, Robbins J, Lingren T. et al. An end-to-end hybrid algorithm for automated medication discrepancy detection.. BMC Med Inform Decis Mak 2015; May 6 15: 37.
  • 3 Luo Y, Xin Y, Hochberg E, Joshi R, Uzuner O, Szolovits P. Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 1009-19.
  • 4 Ni Y, Kennebeck S, Dexheimer JW, McAneney CM, Tang H, Lingren T. et al. Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.. J Am Med Inform Assoc 2015; Jan 22 (Suppl. 01) 166-78.
  • 5 Oronoz M, Gojenola K, Pérez A, de Ilarraza AD, Casillas A. On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions.. J Biomed Inform 2015; Aug 56: 318-32.
  • 6 Prud’hommeaux E, Roark B. Graph-Based Word Alignment for Clinical Language Evaluation.. Computational Linguistics 2015; Dec 41 4: 549-78.
  • 7 Journal of Biomedical Informatics.. Volume 58, Supplement, December 2015, Proceedings of the 2014 i2b2/UTHealth Shared-Tasks and Workshop on Challenges in Natural Language Processing for Clinical Data.
  • 8 Pradhan S, Elhadad N, South BR, Martinez D, Christensen L, Vogel A. et al. Evaluating the state of the art in disorder recognition and normalization of the clinical narrative.. J Am Med Inform Assoc 2015; Jan 22 (Suppl. 01) 143-54.
  • 9 Lin C, Dligach D, Miller TA, Bethard S, Savova GK. Multilayered temporal modeling for the clinical domain.. J Am Med Inform Assoc 2016; Mar 23 (Suppl. 02) 387-95.
  • 10 Chapman WW, Nadkarni PM, Hirschman L, D’Avolio LW, Savova GK, Uzuner O. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions.. J Am Med Inform Assoc 2011; SepOct 18 (Suppl. 05) 540-3.
  • 11 Zheng K, Vydiswaran VG, Liu Y, Wang Y, Stubbs A, Uzuner Ö. et al. Ease of adoption of clinical natural language processing software: An evaluation of five systems.. J Biomed Inform 2015; Dec 58 Suppl: S189-96.
  • 12 Suominen H, Zhou L, Hanlen L, Ferraro G. Benchmarking clinical speech recognition and information extraction: new data, methods, and evaluations.. JMIR Med Inform 2015; Apr 27 3 (Suppl. 02) e19.
  • 13 Xiao B, Imel ZE, Georgiou PG, Atkins DC, Narayanan SS. “Rate My Therapist”: Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing.. PLoS One 2015; Dec 2 10 (Suppl. 012) e0143055.
  • 14 Liu X, Chen H. A research framework for pharmacovigilance in health social media: Identification and evaluation of patient adverse drug event reports.. J Biomed Inform 2015; Dec 58: 268-79.
  • 15 Nikfarjam A, Sarker A, O’Connor K, Ginn R, Gonzalez G. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.. J Am Med Inform Assoc 2015; May 22 (Suppl. 03) 671-81.
  • 16 Yu S, Liao KP, Shaw SY, Gainer VS, Churchill SE, Szolovits P. et al. Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 993-1000.
  • 17 Mehrabi S, Krishnan A, Sohn S, Roch AM, Schmidt H, Kesterson J. et al. DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx.. J Biomed Inform 2015; Apr 54: 213-9.
  • 18 Wu S, Miller T, Masanz J, Coarr M, Halgrim S, Carrell D. et al. Negation’s not solved: generalizability versus optimizability in clinical natural language processing.. PLoS One 2014; Nov 13 9 (Suppl. 11) e112774.
  • 19 Jiang M, Huang Y, Fan JW, Tang B, Denny J, Xu H. Parsing clinical text: how good are the state-of-the-art parsers?. BMC Med Inform Decis Mak 2015; 15 (Suppl. 01) S2.
  • 20 Wang Y, Pakhomov S, Ryan JO, Melton GB. Domain adaption of parsing for operative notes.. J Biomed Inform 2015; Apr 54: 1-9.
  • 21 Zhang Y, Tang B, Jiang M, Wang J, Xu H. Domain adaptation for semantic role labeling of clinical text.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 967-79.
  • 22 Moon S, McInnes B, Melton GB. Challenges and practical approaches with word sense disambiguation of acronyms and abbreviations in the clinical domain.. Healthc Inform Res 2015; Jan 21 (Suppl. 01) 35-42.
  • 23 Wu Y, Denny JC, Rosenbloom ST, Miller RA, Giuse DA, Song M. et al. A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time.. Appl Clin Inform 2015; Jun 3 6 (Suppl. 02) 364-74.
  • 24 Sun W, Rumshisky A, Uzuner O. Normalization of relative and incomplete temporal expressions in clinical narratives.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 1001-8.
  • 25 Lin C, Karlson EW, Dligach D, Ramirez MP, Miller TA, Mo H. et al. Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record.. J Am Med Inform Assoc 2015; Apr 22 (Suppl. 01) e151-61.
  • 26 McCoy TH, Castro VM, Cagan A, Roberson AM, Kohane IS, Perlis RH. Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study.. PLoS One 2015; Aug 24 10 (Suppl. 08) e0136341.
  • 27 Meng F, Morioka C. Automating the generation of lexical patterns for processing free text in clinical documents.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 980-6.
  • 28 Shivade C, Hebert C, Lopetegui M, de Marneffe MC, Fosler-Lussier E, Lai AM. Textual inference for eligibility criteria resolution in clinical trials.. J Biomed Inform 2015; Dec 58 Suppl: S211-8.
  • 29 Koopman B, Zuccon G, Nguyen A, Bergheim A, Grayson N. Automatic ICD-10 classification of cancers from free-text death certificates.. Int J Med Inform 2015; Nov 84 (Suppl. 011) 956-65.
  • 30 McCoy TH, Castro VM, Rosenfield HR, Cagan A, Kohane IS, Perlis RH. A clinical perspective on the relevance of research domain criteria in electronic health records.. Am J Psychiatry 2015; Apr 172 (Suppl. 04) 316-20.
  • 31 Joffe E, Pettigrew EJ, Herskovic JR, Bearden CF, Bernstam EV. Expert guided natural language processing using one-class classification.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 962-6.