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A Systematic Study on Extraction of Temporal Relation from Clinical Free Text

by Vani K.B., Lokesh M.R.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 41
Year of Publication: 2024
Authors: Vani K.B., Lokesh M.R.
10.5120/ijca2024923999

Vani K.B., Lokesh M.R. . A Systematic Study on Extraction of Temporal Relation from Clinical Free Text. International Journal of Computer Applications. 186, 41 ( Sep 2024), 26-39. DOI=10.5120/ijca2024923999

@article{ 10.5120/ijca2024923999,
author = { Vani K.B., Lokesh M.R. },
title = { A Systematic Study on Extraction of Temporal Relation from Clinical Free Text },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2024 },
volume = { 186 },
number = { 41 },
month = { Sep },
year = { 2024 },
issn = { 0975-8887 },
pages = { 26-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number41/a-systematic-study-on-extraction-of-temporal-relation-from-clinical-free-text/ },
doi = { 10.5120/ijca2024923999 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-09-30T23:02:32.000434+05:30
%A Vani K.B.
%A Lokesh M.R.
%T A Systematic Study on Extraction of Temporal Relation from Clinical Free Text
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 41
%P 26-39
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clinical Assessment and decision making mainly depends on Temporal Relations that exists between clinical event and activities of treatment prescribed. Temporal Relation extraction is a challenging task due to complexities associated with natural language processing techniques, representational ways for temporal data related to clinical activities, methodical approaches followed to extract temporal relations and temporal reasoning. In this work, we propose review of temporal relation extraction in clinical text. We analyzed around 118 articles via an exhaustive search of semanticscholar.org, PubMed, DBLP computer science Bibliography between 2018 to 2023. Relevant studies were made concerned to data sets and methodical approaches incorporated to extract temporal information. A thorough examination of selected papers was made to collect information on TLINK types, data sources, features selection methods used, DocTimeRel, Candidate pair generations and reported performance. Most state of the art is based on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. Performance of Tlink extraction is dependent parameter of underlying mechanisms involved in temporal expression identification, temporal events recognitions and mechanisms used to extract temporal relations. F-score for identifying the temporal relation is observed to be in the range of 80% to 91.1%. Most works frequently used TLINKS are ‘before’, ‘after’, ‘overlap’ and ‘contains’ leaving a scope to extend the use of other TLINKS such as ‘started by’, ‘finished by’ ’precedes’ and so on. Machine learning based models and Deep learning-based models were the most commonly adopted techniques for extraction of temporal relations. Dataset Imbalance because of candidate pair generation and task complexity affects system’s performance leaving a scope for research. Most publications worked so far resides on same datasets, which shows a need for design of experiments on new kind of annotations.

References
  1. Peter B. Jensen, Lars J. Jensen, and Søren Brunak. 2012. Mining electronic health records: Towards better research applications and clinical care. Nature Reviews Genetics 13, 6 (2012), 395–405. DOI:https://doi.org/10.1038/nrg3208
  2. Daniel Capurro, Meliha Yetisgen, Erik van Eaton, Robert Black, and Peter Tarczy-Hornoch. 2014. Availability of structured and unstructured clinical data for comparative effectiveness research and quality improvement: A multisite assessment. EGEMS 2, 1 (2014), 1079. DOI:https://doi.org/10.13063/2327-9214.1079
  3. Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sungrim Moon, Feichen Shen, Naveed Afzal, Sijia Liu, Yuqun Zeng, Saeed Mehrabi, Sunghwan Sohn, and Hongfang Liu. 2018. Clinical information extraction applications: A literature review. Journal of Biomedical Informatics 77 (2018), 34–49. DOI:https://doi.org/10.1016/j.jbi.2017.11.011
  4. Kory Kreimeyer, Matthew Foster, Abhishek Pandey, Nina Arya, Gwendolyn Halford, Sandra F Jones, Richard Forshee, Mark Walderhaug, and Taxiarchis Botsis. 2017. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. Journal of Biomedical Informatics 73 (2017), 14–29. DOI:https://doi.org/10.1016/j.jbi.2017.07.012
  5. Sumithra Velupillai, Danielle Mowery, Brett South, Maria Kvist, and Hercules Dalianis. 2015. Recent advances in clinical natural language processing in support of semantic analysis. Yearbook of medical informatics 10 (2015), 183– 93. DOI:https://doi.org/10.15265/IY-2015-009 ACM Computing Surveys, Vol. 54, No. 7, Article 144. Publication date: September 2021. 144:28 Y. B. Gumiel et al.
  6. William F. Styler IV, Steven Bethard, Sean Finan, Martha Palmer, Sameer Pradhan, Piet C de Groen, Brad Erickson, Timothy Miller, Chen Lin, Guergana Savova, and James Pustejovsky. 2014. Temporal annotation in the clinical domain. Transactions of the Association for Computational Linguistics 2 (2014), 143–154. DOI:https://doi.org/10.1162/ tacl_a_00172
  7. Azadeh Nikfarjam, Ehsan Emadzadeh, and Graciela Gonzalez. 2013. Towards generating a patient’s timeline: Extracting temporal relationships from clinical notes. Journal of Biomedical Informatics 46 (2013), S40–S47. DOI:https: //doi.org/10.1016/j.jbi.2013.11.001
  8. Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T. Dudley, Alberto Lavelli, Fabio Rinaldi, and Venet Osmani. 2019. Natural language processing of clinical notes on chronic diseases: Systematic review. JMIR Med Inform 7, 2 (2019), e12239. DOI:https://doi.org/10.2196/12239
  9. World Health Organization. 2020. Noncommunicable Diseases: Progress Monitor 2020. World Health Organization. vi, 224 pages.
  10. James F. Allen. 1983. Maintaining knowledge about temporal intervals. Communications of the ACM 26, 11 (1983), 832–843. DOI:https://doi.org/10.1145/182.358434
  11. Buzhou Tang, Yonghui Wu, Min Jiang, Yukun Chen, Joshua C. Denny, and Hua Xu. 2013. A hybrid system for temporal information extraction from clinical text. Journal of the American Medical Informatics Association 20, 5 (2013), 828–835. DOI:https://doi.org/10.1136/amiajnl-2013-001635
  12. Chen Lin, Dmitriy Dligach, Timothy A. Miller, Steven Bethard, and Guergana K. Savova. 2016. Multilayered temporal modeling for the clinical domain. Journal of the American Medical Informatics Association 23, 2 (2016), 387–395. DOI:https://doi.org/10.1093/jamia/ocv113
  13. Robert Leaman, Ritu Khare, and Zhiyong Lu. 2015. Challenges in clinical natural language processing for automated disorder normalization. Journal of Biomedical Informatics 57, C (2015), 28–37. DOI:https://doi.org/10.1016/j.jbi.2015. 07.010
  14. Stephane M. Meystre, Guergana K. Savova, Karin C. Kipper-Schuler, and John F. Hurdle. 2008. Extracting information from textual documents in the electronic health record: A review of recent research. Yearbook of Medical Informatics (2008), 128–144.
  15. Weiyi Sun, Anna Rumshisky, and Ozlem Uzuner. 2013. Evaluating temporal relations in clinical text: 2012 i2b2 Challenge. Journal of the American Medical Informatics Association 20, 5 (2013), 806–813. DOI:https://doi.org/10. 1136/amiajnl-2013-001628
  16. Steven Bethard, Leon Derczynski, Guergana Savova, James Pustejovsky, and Marc Verhagen. 2015. SemEval-2015 Task 6: Clinical TempEval. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). Association for Computational Linguistics, Denver, Colorado, 806–814. DOI:https://doi.org/10.18653/v1/S15-2136
  17. Steven Bethard, Guergana Savova, Wei-Te Chen, Leon Derczynski, James Pustejovsky, and Marc Verhagen. 2016. SemEval-2016 Task 12: Clinical TempEval. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1052–1062. DOI:https://doi.org/10. 18653/v1/S16-1165
  18. Steven Bethard, Guergana Savova, Martha Palmer, and James Pustejovsky. 2017. SemEval-2017 Task 12: Clinical TempEval. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). Association for Computational Linguistics, Vancouver, Canada, 565–572. DOI:https://doi.org/10.18653/v1/S17-2093
  19. David Moher, Alessandro Liberati, Jennifer Tetzlaff, Douglas G. Altman, and The PRISMA Group. 2009. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Medicine 6, 7 (2009), 1–6. DOI:https://doi.org/10.1371/journal.pmed.1000097
  20. Weiyi Sun, Anna Rumshisky, and Ozlem Uzuner. 2013. Temporal reasoning over clinical text: The state of the art. Journal of the American Medical Informatics Association 20, 5 (2013), 814–819. DOI:https://doi.org/10.1136/amiajnl2013-001760
  21. Ghada Alfattni, Niels Peek, and Goran Nenadic. 2020. Extraction of temporal relations from clinical free text: A systematic review of current approaches. Journal of Biomedical Informatics 108 (2020), 103488. DOI:https://doi.org/ 10.1016/j.jbi.2020.103488
  22. James Pustejovsky, Kiyong Lee, Harry Bunt, and Laurent Romary. 2010. ISO-TimeML: An international standard for semantic annotation. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA), Valletta, Malta, 394–397.
  23. Yao Cheng, Peter Anick, Pengyu Hong, and Nianwen Xue. 2013. Temporal relation discovery between events and temporal expressions identified in clinical narrative. Journal of Biomedical Informatics 46 (2013), S48–S53. DOI:https: //doi.org/10.1016/j.jbi.2013.09.010
  24. James Pustejovsky, Robert Knippen, Jessica Littman, and Roser Saurí. 2005. Temporal and event information in natural language text. Language Resources and Evaluation 39, 2 (2005), 123–164. DOI:https://doi.org/10.1007/s10579- 005-7882-7 ACM Computing Surveys, Vol. 54, No. 7, Article 144. Publication date: September 2021. Temporal Relation Extraction in Clinical Texts: A Systematic Review 144:29
  25. Marc Verhagen. 2005. Temporal closure in an annotation environment. Language Resources and Evaluation 39, 2 (2005), 211–241. DOI:https://doi.org/10.1007/s10579-005-7884-5
  26. Leon Derczynski. 2016. Representation and learning of temporal relations. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. The COLING 2016 Organizing Committee, Osaka, Japan, 1937–1948.
  27. James Pustejovsky and Amber Stubbs. 2011. Increasing informativeness in temporal annotation. In Proceedings of the 5th Linguistic Annotation Workshop. Association for Computational Linguistics, Portland, Oregon, 152–160.
  28. Abdulrahman Khalifa, Sumithra Velupillai, and Stephane Meystre. 2016. UtahBMI at SemEval-2016 Task 12: Extracting temporal information from clinical text. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1256–1262. DOI:https: //doi.org/10.18653/v1/S16-1195
  29. Guergana K. Savova, James J. Masanz, Philip V. Ogren, Jiaping Zheng, Sunghwan Sohn, Karin C. Kipper-Schuler, and Christopher G. Chute. 2010. Mayo clinical text analysis and knowledge extraction system (cTAKES): Architecture, component evaluation and applications. Journal of the American Medical Informatics Association 17, 5 (2010), 507– 513. DOI:https://doi.org/10.1136/jamia.2009.001560
  30. Alan R. Aronson. 2001. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proceedings AMIA Symposium (2001), 17–21.
  31. Julien Tourille, Olivier Ferret, Xavier Tannier, and Aurélie Névéol. 2017. Temporal information extraction from clinical text. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Association for Computational Linguistics, Valencia, Spain, 739–745.
  32. Wei Wang, Kory Kreimeyer, Emily Jane Woo, Robert Ball, Matthew Foster, Abhishek Pandey, John Scott, and Taxiarchis Botsis. 2016. A new algorithmic approach for the extraction of temporal associations from clinical narratives with an application to medical product safety surveillance reports. Journal of Biomedical Informatics 62 (2016), 78–89. DOI:https://doi.org/10.1016/j.jbi.2016.06.006
  33. Timothy Miller, Steven Bethard, Dmitriy Dligach, Sameer Pradhan, Chen Lin, and Guergana Savova. 2013. Discovering temporal narrative containers in clinical text. In Proceedings of the 2013 Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics, Sofia, Bulgaria, 18–26.
  34. Chen Lin, Timothy Miller, Alvin Kho, Steven Bethard, Dmitriy Dligach, Sameer Pradhan, and Guergana Savova. 2014. Descending-path convolution kernel for syntactic structures. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Baltimore, Maryland, 81–86. DOI:https://doi.org/10.3115/v1/P14-2014
  35. Gandhimathi Moharasan and Tu-Bao Ho. 2019. Extraction of temporal information from clinical narratives. Journal of Healthcare Informatics Research 3, 2 (2019), 220–244. DOI:https://doi.org/10.1007/s41666-019-00049-0
  36. Thanat Chokwijitkul, Anthony Nguyen, Hamed Hassanzadeh, and Siegfried Perez. 2018. Identifying risk factors for heart disease in electronic medical records: A deep learning approach. In Proceedings of the BioNLP 2018 Workshop. Association for Computational Linguistics, Melbourne, Australia, 18–27. DOI:https://doi.org/10.18653/v1/W18-2303
  37. Zixu Wang, Julia Ive, Sumithra Velupillai, and Lucia Specia. 2019. Is artificial data useful for biomedical Natural Language Processing algorithms?. In Proceedings of the 18th BioNLP Workshop and Shared Task. Association for Computational Linguistics, Florence, Italy, 240–249. DOI:https://doi.org/10.18653/v1/W19-5026
  38. Hee Jin Lee, Yaoyun Zhang, Min Jiang, Jun Xu, Cui Tao, and Hua Xu. 2018. Identifying direct temporal relations between time and events from clinical notes. BMC Medical Informatics and Decision Making 18, Suppl 2 (2018), 49. DOI:https://doi.org/10.1186/s12911-018-0627-5
  39. Dong Xu, Meizhuo Zhang, Tianwan Zhao, Chen Ge, Weiguo Gao, Jia Wei, and Kenny Q. Zhu. 2015. Data-driven information extraction from Chinese electronic medical records. PLOS ONE 10, 8 (2015), 1–18. DOI:https://doi.org/ 10.1371/journal.pone.0136270
  40. Jia Su, Jinpeng Hu, Jingchi Jiang, Jing Xie, Yang Yang, Bin He, Jinfeng Yang, and Yi Guan. 2019. Extraction of risk factors for cardiovascular diseases from Chinese electronic medical records. Computer Methods and Programs in Biomedicine 172 (2019), 1–10.
  41. Zengjian Liu, Xiaolong Wang, Qingcai Chen, Buzhou Tang, and Hua Xu. 2019. Temporal indexing of medical entity in Chinese clinical notes. BMC Medical Informatics and Decision Making 19, 1 (2019), 17. DOI:https://doi.org/10.1186/ s12911-019-0735-x
  42. Xiaohui Zhang, Yaoyun Zhang, Qin Zhang, Yuankai Ren, Tinglin Qiu, Jianhui Ma, and Qiang Sun. 2019. Extracting comprehensive clinical information for breast cancer using deep learning methods. International Journal of Medical Informatics 132 (2019), 103985. DOI:https://doi.org/10.1016/j.ijmedinf.2019.103985
  43. Jae-Wook Seol, Wangjin Yi, Jinwook Choi, and Kyung Soon Lee. 2017. Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries. International Journal of Medical Informatics 98 (2017), 1–12. DOI:https://doi.org/10.1016/j.ijmedinf.2016.10.021 ACM Computing Surveys, Vol. 54, No. 7, Article 144. Publication date: September 2021. 144:30 Y. B. Gumiel et al.
  44. Wangjin Lee and Jinwook Choi. 2018. Temporal segmentation for capturing snapshots of patient histories in Korean clinical narrative. Healthcare Informatics Research 24, 3 (2018), 179–186. DOI:https://doi.org/10.4258/hir.2018.24.3.179
  45. Zubair Afzal, Ewoud Pons, Ning Kang, Miriam C. J. M. Sturkenboom, Martijn J. Schuemie, and Jan A. Kors. 2014. ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus. BMC Bioinformatics 15, 1 (2014), 373. DOI:https://doi.org/10.1186/s12859-014- 0373-3
  46. Natalia Viani, Timothy A. Miller, Carlo Napolitano, Silvia G. Priori, Guergana K. Savova, Riccardo Bellazzi, and Lucia Sacchi. 2019. Supervised methods to extract clinical events from cardiology reports in Italian. Journal of Biomedical Informatics 95 (2019), 103219. DOI:https://doi.org/10.1016/j. jbi.2019.103219
  47. Aron Henriksson, Maria Kvist, Hercules Dalianis, and Martin Duneld. 2015. Identifying adverse drug event information in clinical notes with distributional semantic representations of context. Journal of Biomedical Informatics 57 (2015), 333–349. DOI:https://doi.org/10.1016/j.jbi.2015.08.013
  48. Marjan Najafabadipour, Massimiliano Zanin, Alejandro Rodríguez-González, Maria Torrente, Beatriz Nuñez García, Juan Luis Cruz Bermudez, Mariano Provencio, and Ernestina Menasalvas. 2020. Reconstructing the patient’s natural history from electronic health records. Artificial Intelligence in Medicine 105 (2020), 101860. DOI:https://doi.org/10. 1016/j.artmed.2020.101860
  49. Leonardo Campillos, Louise Deléger, Cyril Grouin, Thierry Hamon, Anne-Laure Ligozat, and Aurélie Névéol. 2018. A French clinical corpus with comprehensive semantic annotations: Development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT). Language Resources and Evaluation 52, 2 (2018), 571–601. DOI:https://doi. org/10.1007/s10579-017-9382-y
  50. Srinivasan V. Iyer, Rave Harpaz, Paea LePendu, Anna Bauer-Mehren, and Nigam H. Shah. 2014. Mining clinical text for signals of adverse drug-drug interactions. Journal of the American Medical Informatics Association 21, 2 (2014), 353–362. DOI:http://dx.doi.org/10.1136/amiajnl-2013-001612
  51. Srinivasan V. Iyer, Paea Lependu, Rave Harpaz, Anna Bauer-Mehren, and Nigam H. Shah. 2013. Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records. AMIA Joint Summits on Translational Science Proceedings 2013 (2013), 83–87.
  52. Yi Liu, Paea Lependu, Srinivasan Iyer, and Nigam H. Shah. 2012. Using temporal patterns in medical records to discern adverse drug events from indications. AMIA Joint Summits on Translational Science Proceedings 2012 (2012), 47–56.
  53. Hee-Jin Lee, Min Jiang, Yonghui Wu, Christian M. Shaffer, John H. Cleator, Eitan A. Friedman, Joshua P. Lewis, Dan M. Roden, Josh Denny, and Hua Xu. 2017. A comparative study of different methods for automatic identification of clopidogrel-induced bleedings in electronic health records. AMIA Joint Summits on Translational Science Proceedings 2017 (2017), 185–192.
  54. Cosmin A. Bejan, Lucy Vanderwende, Heather L. Evans, Mark M. Wurfel, and Meliha Yetisgen-Yildiz. 2013. Ontime clinical phenotype prediction based on narrative reports. AMIA Annual Symposium Proceedings 2013 (2013), 103–110.
  55. Stephen T. Wu, Young J. Juhn, Sunghwan Sohn, and Hongfang Liu. 2014. Patient-level temporal aggregation for textbased asthma status ascertainment. Journal of the American Medical Informatics Association 21, 5 (2014), 876–884. DOI:https://doi.org/10.1136/amiajnl-2013-002463
  56. Henk Harkema, John N. Dowling, Tyler Thornblade, and Wendy W. Chapman. 2009. ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports. Journal of Biomedical Informatics 42, 5 (2009), 839–851. DOI:https://doi.org/10.1016/j.jbi.2009.05.002
  57. Rob Gaizauskas, Henk Harkema, Mark Hepple, and Andrea Setzer. 2006. Task-oriented extraction of temporal information: The case of clinical narratives. In 13th International Symposium on Temporal Representation and Reasoning (TIME’06). 188–195. DOI:https://doi.org/10.1109/TIME. 2006.27
  58. Nai-Wen Chang, Hong-Jie Dai, Jitendra Jonnagaddala, Chih-Wei Chen, Richard Tzong-Han Tsai, and Wen-Lian Hsu. 2015. A context-aware approach for progression tracking of medical concepts in electronic medical records. Journal of Biomedical Informatics 58, S (2015), S150–S157. DOI:https://doi.org/10.1016/j.jbi.2015.09.013
  59. Qingcai Chen, Haodi Li, Buzhou Tang, Xiaolong Wang, Xin Liu, Zengjian Liu, Shu Liu, Weida Wang, Qiwen Deng, Suisong Zhu, Yangxin Chen, and Jingfeng Wang. 2015. An automatic system to identify heart disease risk factors in clinical texts over time. Journal of Biomedical Informatics 58 (2015), S158–S163. DOI:https://doi.org/10.1016/j.jbi. 2015.09.002
  60. James Cormack, Chinmoy Nath, David Milward, Kalpana Raja, and Siddhartha R. Jonnalagadda. 2015. Agile text mining for the 2014 i2b2/UTHealth Cardiac Risk Factors Challenge. Journal of Biomedical Informatics 58 (2015), S120–S127. DOI:https://doi.org/10.1016/j.jbi.2015.06.030
  61. Travis Goodwin and Sanda M. Harabagiu. 2015. A probabilistic reasoning method for predicting the progression of clinical findings from electronic medical records. AMIA Joint Summits on Translational Science Proceedings 2015 (2015), 61–65. ACM Computing Surveys, Vol. 54, No. 7, Article 144. Publication date: September 2021. Temporal Relation Extraction in Clinical Texts: A Systematic Review 144:31
  62. Cyril Grouin, Véronique Moriceau, and Pierre Zweigenbaum. 2015. Combining glass box and black box evaluations in the identification of heart disease risk factors and their temporal relations from clinical records. Journal of Biomedical Informatics 58 (2015), S133–S142. DOI:https://doi.org/10.1016/j.jbi.2015.06.014
  63. Jitendra Jonnagaddala, Siaw-Teng Liaw, Pradeep Ray, Manish Kumar, Hong-Jie Dai, and Chien-Yeh Hsu. 2015. Identification and progression of heart disease risk factors in diabetic patients from longitudinal electronic health records. BioMed Research International 2015 (2015), 636371. DOI:https://doi.org/10.1155/2015/636371
  64. George Karystianis, Azad Dehghan, Aleksandar Kovacevic, John A. Keane, and Goran Nenadic. 2015. Using local lexicalized rules to identify heart disease risk factors in clinical notes. Journal of Biomedical Informatics 58 Suppl, Suppl (2015), S183–S188. DOI:https://doi.org/10.1016/j.jbi.2015.06.013
  65. Abdulrahman Khalifa and Stéphane Meystre. 2015. Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes. Journal of Biomedical Informatics 58, S (2015), S128–S132. DOI:https://doi.org/10.1016/j.jbi.2015.08.002
  66. Kirk Roberts, Sonya E. Shooshan, Laritza Rodriguez, Swapna Abhyankar, Halil Kilicoglu, and Dina DemnerFushman. 2015. The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs. Journal of Biomedical Informatics 58, S (2015), S111–S119. DOI:https://doi.org/10.1016/j.jbi.2015.06.010
  67. Chaitanya P. Shivade, Pranav Malewadkar, Eric Fosler-Lussier, and Albert M. Lai. 2015. Comparison of UMLS terminologies to identify risk of heart disease using clinical notes. Journal of Biomedical Informatics 58 (2015), S103–S110. DOI:https://doi.org/10.1016/j.jbi.2015.08.025
  68. Manabu Torii, Jung wei Fan, Wei li Yang, Theodore Lee, Matthew T. Wiley, Daniel S. Zisook, and Yang Huang. 2015. Risk factor detection for heart disease by applying text analytics in electronic medical records. Journal of Biomedical Informatics 58 (2015), S164–S170. DOI:https://doi.org/10.1016/j.jbi.2015.08.011
  69. Jay Urbain. 2015. Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models. Journal of Biomedical Informatics 58 (2015), S143–S149. DOI:https://doi.org/10.1016/j.jbi.2015.08. 009
  70. Hui Yang and Jonathan M. Garibaldi. 2015. A hybrid model for automatic identification of risk factors for heart disease. Journal of Biomedical Informatics 58 Suppl, Suppl (2015), S171–S182. DOI:https://doi.org/10.1016/j.jbi.2015. 09.006
  71. Yung-Chun Chang, Hong-Jie Dai, Johnny Chi-Yang Wu, Jian-Ming Chen, Richard Tzong-Han Tsai, and Wen-Lian Hsu. 2013. TEMPTING System: A hybrid method of rule and machine learning for temporal relation extraction in patient discharge summaries. Journal of Biomedical Informatics 46 (2013), S54–S62. DOI:https://doi.org/10.1016/j.jbi. 2013.09.007
  72. Colin Cherry, Xiaodan Zhu, Joel Martin, and Berry de Bruijn. 2013. À la recherche du temps perdu: Extracting temporal relations from medical text in the 2012 i2b2 NLP challenge. Journal of the American Medical Informatics Association 20, 5 (2013), 843–848. DOI:https://doi.org/10.1136/amiajnl-2013-001624
  73. Jennifer D’Souza and Vincent Ng. 2013. Classifying temporal relations in clinical data: A hybrid, knowledge-rich approach. Journal of Biomedical Informatics 46 (2013), S29–S39. DOI:https://doi.org/10.1016/j.jbi.2013.08.003
  74. Jennifer D’Souza and Vincent Ng. 2014. Knowledge-rich temporal relation identification and classification in clinical notes. Database 2014 (11 2014). DOI:https://doi.org/10.1093/database/bau109
  75. Jennifer D’Souza and Vincent Ng. 2014. Annotating inter-sentence temporal relations in clinical notes. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC’14). European Language Resources Association (ELRA), Reykjavik, Iceland, 2758–2765.
  76. Cyril Grouin, Natalia Grabar, Thierry Hamon, Sophie Rosset, Xavier Tannier, and Pierre Zweigenbaum. 2013. Eventual situations for timeline extraction from clinical reports. Journal of the American Medical Informatics Association 20, 5 (2013), 820–827. DOI:https://doi.org/10.1136/amiajnl-2013-001627
  77. Zhijing Li, Chen Li, Yu Long, and Xuan Wang. 2020. A system for automatically extracting clinical events with temporal information. BMC Medical Informatics and Decision Making 20, 1 (2020), 198. DOI:https://doi.org/10.1186/ s12911-020-01208-9
  78. Danielle Mowery, Henk Harkema, John Dowling, Jonathan Lustgarten, and Wendy Chapman. 2009. Distinguishing historical from current problems in clinical reports—which textual features help?. In Proceedings of the BioNLP 2009 Workshop. Association for Computational Linguistics, Boulder, Colorado, 10–18.
  79. Preethi Raghavan, Albert Lai, and Eric Fosler-Lussier. 2012. Learning to temporally order medical events in clinical text. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Jeju Island, Korea, 70–74.
  80. Preethi Raghavan, Eric Fosler-Lussier, and Albert Lai. 2012. Temporal classification of medical events. In BioNLP: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics, Montréal, Canada, 29–37. ACM Computing Surveys, Vol. 54, No. 7, Article 144. Publication date: September 2021. 144:32 Y. B. Gumiel et al.
  81. Preethi Raghavan, Eric Fosler-Lussier, and Albert Lai. 2012. Exploring semi-supervised coreference resolution of medical concepts using semantic and temporal features. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Montréal, Canada, 731–741.
  82. Chen Lin, Elizabeth W. Karlson, Dmitriy Dligach, Monica P. Ramirez, Timothy A. Miller, Huan Mo, Natalie S. Braggs, Andrew Cagan, Vivian Gainer, Joshua C. Denny, and Guergana K. Savova. 2015. Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record. Journal of the American Medical Informatics Association 22, e1 (2015), e151–161. DOI:https://doi.org/10.1136/amiajnl-2014- 002642
  83. Marcia Barros, Andre Lamurias, Gonçalo Figueiro, Marta Antunes, Joana Teixeira, Alexandre Pinheiro, and Francisco M. Couto. 2016. ULISBOA at SemEval-2016 Task 12: Extraction of temporal expressions, clinical events and relations using IBEnt. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1263–1267. DOI:https://doi.org/10.18653/v1/S16-1196
  84. Tommaso Caselli and Roser Morante. 2016. VUACLTL at SemEval 2016 Task 12: A CRF pipeline to clinical TempEval. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1241–1247. DOI:https://doi.org/10.18653/v1/S16-1193
  85. Veera Raghavendra Chikka. 2016. CDE-IIITH at SemEval-2016 Task 12: Extraction of temporal information from clinical documents using machine learning techniques. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1237–1240. DOI:https: //doi.org/10.18653/v1/S16-1192
  86. Arman Cohan, Kevin Meurer, and Nazli Goharian. 2016. GUIR at SemEval-2016 Task 12: Temporal information processing for clinical narratives. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1248–1255. DOI:https://doi.org/10.18653/v1/S16- 1194
  87. Dmitriy Dligach, Timothy Miller, Chen Lin, Steven Bethard, and Guergana Savova. 2017. Neural temporal relation extraction. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Association for Computational Linguistics, Valencia, Spain, 746–751.
  88. Jason Fries. 2016. Brundlefly at SemEval-2016 Task 12: Recurrent neural networks vs. Joint Inference for Clinical Temporal Information Extraction. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1274–1279. DOI:https://doi.org/10. 18653/v1/S16-1198
  89. Diana Galvan, Koji Matsuda, Naoaki Okazaki, and Kentaro Inui. 2020. Empirical exploration of the challenges in temporal relation extraction from clinical text. Journal of Natural Language Processing 27, 2 (2020), 383–409. DOI:https://doi.org/10.5715/jnlp.27.383
  90. Diana Galvan, Naoaki Okazaki, Koji Matsuda, and Kentaro Inui. 2018. Investigating the challenges of temporal relation extraction from clinical text. In Proceedings of the 9th International Workshop on Health Text Mining and Information Analysis. Association for Computational Linguistics, Brussels, Belgium, 55–64. DOI:https://doi.org/10. 18653/v1/W18-5607
  91. Cyril Grouin and Véronique Moriceau. 2016. LIMSI at SemEval-2016 Task 12: Machine-learning and temporal information to identify clinical events and time expressions. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1225–1230. DOI:https://doi.org/10.18653/v1/S16-1190
  92. Serena Jeblee and Graeme Hirst. 2018. Listwise temporal ordering of events in clinical notes. In Proceedings of the 9th International Workshop on Health Text Mining and Information Analysis. Association for Computational Linguistics, Brussels, Belgium, 177–182. DOI:https://doi.org/10.18653/v1/W18-5620
  93. Catherine Kerr, Terri Hoare, Paula Carroll, and Jakub Mareček. 2020. Integer programming ensemble of temporal relations classifiers. Data Mining and Knowledge Discovery 34, 2 (2020), 533–562. DOI:https://doi.org/10.1007/s10618- 019-00671-x
  94. Hee-Jin Lee, Hua Xu, Jingqi Wang, Yaoyun Zhang, Sungrim Moon, Jun Xu, and Yonghui Wu. 2016. UTHealth at SemEval’16 Task 12: An end-to-end system for temporal information extraction from clinical notes. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1292–1297. DOI:https://doi.org/10.18653/v1/S16-1201
  95. Artuur Leeuwenberg and Marie-Francine Moens. 2018. Word-level loss extensions for neural temporal relation classification. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, Santa Fe, New Mexico, 3436–3447.
  96. Artuur Leeuwenberg and Marie-Francine Moens. 2016. KULeuven-LIIR at SemEval 2016 Task 12: Detecting narrative containment in clinical records. In Proceedings of the 10th International Workshop on Semantic Evaluation ACM Computing Surveys, Vol. 54, No. 7, Article 144. Publication date: September 2021. Temporal Relation Extraction in Clinical Texts: A Systematic Review 144:33 (SemEval’16). Association for Computational Linguistics, San Diego, California, 1280–1285. DOI:https://doi.org/10. 18653/v1/S16-1199
  97. Artuur Leeuwenberg and Marie-Francine Moens. 2017. Structured learning for temporal relation extraction from clinical records. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Association for Computational Linguistics, Valencia, Spain, 1150–1158.
  98. Peng Li and Heng Huang. 2016. UTA DLNLP at SemEval-2016 Task 12: Deep learning based natural language processing system for clinical information identification from clinical notes and pathology reports. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1268–1273. DOI:https://doi.org/10.18653/v1/S16-1197
  99. Chen Lin, Timothy Miller, Dmitriy Dligach, Hadi Amiri, Steven Bethard, and Guergana Savova. 2018. Self-training improves recurrent neural networks performance for temporal relation extraction. In Proceedings of the 9th International Workshop on Health Text Mining and Information Analysis. Association for Computational Linguistics, Brussels, Belgium, 165–176. DOI:https://doi.org/10.18653/v1/W18-5619
  100. Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2016. Improving temporal relation extraction with training instance augmentation. In Proceedings of the 15th Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics, Berlin, Germany, 108–113. DOI:https://doi.org/10. 18653/v1/W16-2914
  101. Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2017. Representations of time expressions for temporal relation extraction with convolutional neural networks. In BioNLP 2017. Association for Computational Linguistics, Vancouver, Canada, 322–327. DOI:https://doi.org/10.18653/v1/W17-2341
  102. Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2019. A BERT-based universal model for both within- and cross-sentence clinical temporal relation extraction. In Proceedings of the 2nd Clinical Natural Language Processing Workshop. Association for Computational Linguistics, Minneapolis, Minnesota, 65–71. DOI:https://doi.org/10.18653/v1/W19-1908
  103. Chen Lin, Timothy Miller, Dmitriy Dligach, Farig Sadeque, Steven Bethard, and Guergana Savova. 2020. A BERTbased one-pass multi-task model for clinical temporal relation extraction. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing. Association for Computational Linguistics, Online, 70–75. DOI:https: //doi.org/10.18653/v1/2020.bionlp-1.7
  104. Sijia Liu, Liwei Wang, Vipin Chaudhary, and Hongfang Liu. 2019. Attention neural model for temporal relation extraction. In Proceedings of the 2nd Clinical Natural Language Processing Workshop. Association for Computational Linguistics, Minneapolis, Minnesota, 134–139. DOI:https://doi.org/10.18653/v1/W19-1917
  105. Julien Tourille, Olivier Ferret, Aurélie Névéol, and Xavier Tannier. 2017. Neural architecture for temporal relation extraction: A Bi-LSTM approach for detecting narrative containers. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Vancouver, Canada, 224–230. DOI:https://doi.org/10.18653/v1/P17-2035
  106. Julien Tourille, Olivier Ferret, Aurélie Névéol, and Xavier Tannier. 2016. LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, San Diego, California, 1136–1142. DOI:https://doi.org/10.18653/v1/S16-1175
  107. Shiyi Zhao, Lishuang Li, Hongbin Lu, Anqiao Zhou, and Shuang Qian. 2019. Associative attention networks for temporal relation extraction from electronic health records. Journal of Biomedical Informatics 99 (2019), 103309. DOI:https://doi.org/10.1016/j.jbi.2019.103309
  108. Po-Yu Huang, Hen-Hsen Huang, Yu-Wun Wang, Ching Huang, and Hsin-Hsi Chen. 2017. NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). Association for Computational Linguistics, Vancouver, Canada, 1010–1013. DOI:https://doi.org/10.18653/v1/S17-2177
  109. Artuur Leeuwenberg and Marie-Francine Moens. 2017. KULeuven-LIIR at SemEval’17 Task 12: Cross-domain temporal information extraction from clinical records. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). Association for Computational Linguistics, Vancouver, Canada, 1030–1034. DOI:https: //doi.org/10.18653/v1/S17-2181
  110. Yu Long, Zhijing Li, Xuan Wang, and Chen Li. 2017. XJNLP at SemEval-2017 Task 12: Clinical temporal information extraction with a hybrid model. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). Association for Computational Linguistics, Vancouver, Canada, 1014–1018. DOI:https://doi.org/10.18653/ v1/S17-2178
  111. Sean MacAvaney, Arman Cohan, and Nazli Goharian. 2017. GUIR at SemEval-2017 Task 12: A framework for cross-domain clinical temporal information extraction. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). Association for Computational Linguistics, Vancouver, Canada, 1024–1029. DOI:https: //doi.org/10.18653/v1/S17-2180 ACM Computing Surveys, Vol. 54, No. 7, Article 144. Publication date: September 2021. 144:34 Y. B. Gumiel et al.
  112. Sarath P. R. Manikandan Ravikiran, and Yoshiki Niwa. 2017. Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). Association for Computational Linguistics, Vancouver, Canada, 1005–1009. DOI:https://doi.org/10. 18653/v1/S17-2176
  113. Julien Tourille, Olivier Ferret, Xavier Tannier, and Aurélie Névéol. 2017. LIMSI-COT at SemEval-17 Task 12: Neural architecture for temporal information extraction from clinical narratives. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). Association for Computational Linguistics, Vancouver, Canada, 597– 602. DOI:https://doi.org/10.18653/v1/S17-2098
  114. Li Rumeng, Jagannatha Abhyuday N, and Yu Hong. 2017. A hybrid neural network model for joint prediction of presence and period assertions of medical events in clinical notes. AMIA Annual Symposium Proceedings 2017 (2017), 1149–1158.
  115. Min Li and Jon Patrick. 2012. Extracting temporal information from electronic patient records. AMIA Annual Symposium Proceedings 2012 (2012), 542–551.
  116. Joshua C. Denny, Josh F. Peterson, Neesha N. Choma, Hua Xu, Randolph A. Miller, Lisa Bastarache, and Neeraja B. Peterson. 2010. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. Journal of the American Medical Informatics Association 17, 4 (2010), 383–388. DOI:https://doi.org/10.1136/jamia. 2010.004804
  117. Sijia Liu, Liwei Wang, Donna Ihrke, Vipin Chaudhary, Cui Tao, Chunhua Weng, and Hongfang Liu. 2017. Correlating lab test results in clinical notes with structured lab data: A case study in HbA1c and glucose. AMIA Joint Summits on Translational Science Proceedings 2017 (2017), 221–228.
  118. Guergana K. Savova, Janet E. Olson, Sean P. Murphy, Victoria L. Cafourek, Fergus J. Couch, Matthew P. Goetz, James N. Ingle, Vera J. Suman, Christopher G. Chute, and Richard M. Weinshilboum. 2012. Automated discovery of drug treatment patterns for endocrine therapy of breast cancer within an electronic medical record. Journal of the American Medical Informatics Association 19, e1 (2012). DOI:https://doi.org/10.1136/amiajnl-2011-000295
  119. Natalia Viani, Joyce Kam, Lucia Yin, Somain Verma, Robert Stewart, Rashmi Patel, and Sumithra Velupillai. 2019. Annotating temporal relations to determine the onset of psychosis symptoms. Studies in Health Technology and Informatics 264 (2019), 418–422. DOI:https://doi.org/10.3233/SHTI190255
  120. Amber Stubbs and Benjamin Harshfield. 2010. Applying the TARSQI toolkit to augment text mining of EHRs. In Proceedings of the 2010 Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics, Uppsala, Sweden, 141–143.
  121. Li Zhou, Simon Parsons, and George Hripcsak. 2008. The evaluation of a temporal reasoning system in processing clinical discharge summaries. Journal of the American Medical Informatics Association 15, 1 (2008), 99–106. DOI:https: //doi.org/10.1197/jamia.M2467
  122. Daniel Capurro, Mario Barbe, Claudio Daza, Josefa Santa María, Javier Trincado, and Ignacio Gomez. 2015. ClinicalTime: Identification of patients with acute kidney injury using temporal abstractions and temporal pattern matching. AMIA Joint Summits on Translational Science Proceedings 2015 (2015), 46–50.
  123. Philip Bramsen, Pawan Deshpande, Yoong Keok Lee, and Regina Barzilay. 2006. Finding temporal order in discharge summaries. AMIA Annual Symposium Proceedings 2006 (2006), 81–85.
  124. Philip Bramsen, Pawan Deshpande, Yoong Keok Lee, and Regina Barzilay. 2006. Inducing temporal graphs. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Sydney, Australia, 189–198.
  125. Preethi Raghavan, Eric Fosler-Lussier, Noémie Elhadad, and Albert M. Lai. 2014. Cross-narrative temporal ordering of medical events. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Baltimore, Maryland, 998–1008. DOI:https://doi.org/10. 3115/v1/P14-1094
  126. Amber Stubbs and Özlem Uzuner. 2015. Annotating risk factors for heart disease in clinical narratives for diabetic patients. Journal of Biomedical Informatics 58 (2015), S78–S91. DOI:https://doi.org/10.1016/j.jbi.2015.05.009
  127. Yan Xu, Yining Wang, Tianren Liu, Junichi Tsujii, and Eric I-Chao Chang. 2013. An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge. Journal of the American Medical Informatics Association 20, 5 (2013), 849–858. DOI:https://doi.org/10.1136/amiajnl-2012-001607
  128. Sumithra Velupillai, Danielle L Mowery, Samir Abdelrahman, Lee Christensen, and Wendy Chapman. 2015. BluLab: Temporal information extraction for the 2015 clinical TempEval challenge. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). Association for Computational Linguistics, Denver, Colorado, 815– 819. DOI:https://doi.org/10.18653/v1/S15-2137
  129. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186. DOI:https://doi.org/10.18653/v1/N19-1423 ACM Computing Surveys, Vol. 54, No. 7, Article 144. Publication date: September 2021. Temporal Relation Extraction in Clinical Texts: A Systematic Review 144:35
  130. Kirk Roberts, Bryan Rink, and Sanda M. Harabagiu. 2013. A flexible framework for recognizing events, temporal expressions, and temporal relations in clinical text. Journal of the American Medical Informatics Association 20, 5 (2013), 867–875. DOI:https://doi.org/10.1136/amiajnl-2013-001619
  131. Roser Saurí, Jessica Moszkowicz, Bob Knippen, Rob Gaizauskas, Andrea Setzer, and James Pustejovsky. 2006. TimeML Annotation Guidelines Version 1.2.1. (2006).
  132. Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to rank: From pairwise approach to listwise approach. In Proceedings of the 24th International Conference on Machine Learning. ACM, New York, NY, 129–136. DOI:https://doi.org/10.1145/1273496.1273513
  133. Makoto Miwa and Mohit Bansal. 2016. End-to-end relation extraction using LSTMs on sequences and tree structures. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, 1105–1116. DOI:https://doi.org/10.18653/v1/P16-1105
  134. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019. BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 4 (2019), 1234–1240. DOI:https://doi.org/10.1093/bioinformatics/btz682
  135. Sunghwan Sohn Dr., Kavishwar B. Wagholikar, Dingcheng Li, Siddhartha R. Jonnalagadda, Cui Tao, Ravikumar Komandur Elayavilli, and Hongfang Liu. 2013. Comprehensive temporal information detection from clinical text: Medical events, time, and TLINK identification. Journal of the American Medical Informatics Association 20, 5 (2013), 836–842. DOI:https://doi.org/10.1136/amiajnl-2013-001622
  136. Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. 2019. Publicly available clinical BERT embeddings. In Proceedings of the 2nd Clinical Natural Language Processing Workshop. Association for Computational Linguistics, Minneapolis, Minnesota, 72–78. DOI:https://doi.org/ 10.18653/v1/W19-1909
  137. Alistair E. W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific Data 3, 1 (2016), 160035. DOI:https://doi.org/10.1038/sdata.2016.35
  138. Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li. 2020. A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge Data Engineering 01 (2020), 1–1. DOI:https://doi.org/10.1109/TKDE.2020.2981314
  139. James Pustejovsky, Patrick Hanks, Roser Saurí, Andrew See, Rob Gaizauskas, Andrea Setzer, Dragomir Radev, Beth Sundheim, David Day, Lisa Ferro, and Marcia Lazo. 2003. The TimeBank corpus. Proceedings of Corpus Linguistics (Jan 2003).
  140. Taylor Cassidy, Bill McDowell, Nathanael Chambers, and Steven Bethard. 2014. An annotation framework for dense event ordering. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Baltimore, Maryland, 501–506. DOI:https://doi.org/10. 3115/v1/P14-2082
  141. Nathanael Chambers, Taylor Cassidy, Bill McDowell, and Steven Bethard. 2014. Dense event ordering with a multipass architecture. Transactions of the Association for Computational Linguistics 2 (2014), 273–284. DOI:https://doi. org/10.1162/tacl_a_00182
  142. Siddharth Vashishtha, Benjamin Van Durme, and Aaron Steven White. 2019. Fine-grained temporal relation extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 2906–2919. DOI: https://doi.org/10.18653/v1/P19-1280
  143. Rujun Han, I-Hung Hsu, Mu Yang, Aram Galstyan, Ralph Weischedel, and Nanyun Peng. 2019. Deep structured neural network for event temporal relation extraction. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, 666–676. DOI:https://doi.org/10.18653/v1/K19-1062
  144. Qiang Ning, Sanjay Subramanian, and Dan Roth. 2019. An improved neural baseline for temporal relation extraction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). Association for Computational Linguistics, Hong Kong, China, 6203–6209. DOI:https://doi.org/10.18653/v1/D19-1642
  145. Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, Louisiana, 2227–2237. DOI:https://doi.org/10.18653/v1/N18-1202
  146. Greg Irving, Ana Luisa Neves, Hajira Dambha-Miller, Ai Oishi, Hiroko Tagashira, Anistasiya Verho, and John Holden. 2017. International variations in primary care physician consultation time: A systematic review of 67 countries. BMJ Open 7, 10 (2017). DOI:https://doi.org/10.1136/bmjopen-2017-017902
  147. Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. (2019). arXiv:1910.01108. Retrieved from https://arxiv.org/abs/1910.01108. ACM Computing Surveys, Vol. 54, No. 7, Article 144. Publication date: September 2021. 144:36 Y. B. Gumiel et al.
  148. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. (2019). arXiv:1907.11692. Retrieved from https://arxiv.org/abs/1907.11692.
  149. Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V. Le. 2019. XLNet: Generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems, 32. Curran Associates, Inc
Index Terms

Computer Science
Information Sciences

Keywords

TLINKS temporal relation extraction complexity candidate pair generation Doctimerel HER (Electronic Health Record)

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