i2b2: Informatics for Integrating Biology & the Bedside - A National Center for Biomedical Computing
NLP Research
Data Sets

Publications Enabled by i2b2 Challenges, 2006-2012

  1. Aramaki E, Imai T, Miyo K, Ohe K. Patient status classification by using rule based sentence extraction and MB35 kNN-based classifier. Proceedings i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2006.
  2. Aramaki E, Miyo K. Automatic deidentification by using sentence features and label consistency. Proceedings i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2006.
  3. Carrero FM, Gomez Hidalgo JM, Puertas E, Mana M, Mata J. Quick prototyping of high performance text classifiers. Proceedings i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2006.
  4. Guillen R. Automated deidentification and categorization of medical records. Proceedings i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2006.
  5. Guo Y, Gaizauskas R, Roberts I, Gaizauskas R, Hepple M. Identifying personal health information using support vector machines. Proceedings i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2006.
  6. Pedersen T. Determining smoker status using supervised and unsupervised learning with lexical features.  i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data. 2006.  Also available as JAMIA on-line data supplement at http://www.jamia.org.
  7. Rekdal M. Identifying smoking status using Argus MLPi2b2. Proceedings i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2006.
  8. Szarvas G, Farkas R, Ivan S, Kocsor A, Busa-Fekete R. Automatic extraction of semantic content from medical discharge summaries. Proceedings i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2006.
  9. Cohen A. Five-way smoking status classification using text hot-spot identification and error-correcting output codes. JAMIA 2007; 15(1):32-5; doi:10.1197/jamia.M2434.
  10. Hara K. Applying a SVM based chunker and a text classifier to the Deid Challenge. i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2007. Also available as a JAMIA on-line data supplement at http://www.jamia.org.
  11. Savova G, Ogren PV, Duffy P, Buntrock J, Chute C. Mayo clinic NLP system for patient smoking status deidentification. JAMIA 2007; Published Online First 10 Dec 2007; 15(1):25-8; doi:10.1197/jamia.M2437.
  12. Szarvas Gy, Farkas R, Busa-Fekete R. State-of-the-art anonymisation of medical records using an iterative machine learning framework. JAMIA 2007; 14(5)574-80.
  13. Uzuner Ö, Luo Y, Szolovits P.  Evaluating the state of the art in automatic de-identification.  JAMIA 2007; 14(5)550-63.
  14. Wellner B, Huyck M, Mardis S, Aberdeen J, Peskin L, Yeh A, Hitzeman J, Hischman L.. Rapidly retargetable approaches to deidentification in medical records. JAMIA 2007; 14:564-73; doi: 10.1197/jamia.M2435.
  15. Barrett N, Weber-Jahnke J. An introduction to MLP driven by the i2b2 challenge. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  16. Califf ME. Combining rules and naïve Bayes for disease classification. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  17. Clark C, Good K, Jezierny L, Macpherson M, Wilson B, Chajewska U. Identifying smokers with a medical extraction system. JAMIA 2008; 15(1):36-9;  doi:10.1197/jamia.M2442.
  18. DeShazo JP, Turner AM. Hands-on NLP: an interactive and user-centered system to classify discharge summaries for obesity and related comorbidities. Proceedings of the i2b2 Workshop on challenges in natural language processing for clinical data, 2008.
  19. Friedlin FJ, McDonald CJ. A software tool for removing patient identifying information from clinical documents. JAMIA 2008; 15(1):601-10; doi:10.1197/jamia.M2702.
  20. Frunza O, Inkpen D. Representation and classification techniques for clinical data focused on obesity and its co-morbidities. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  21. Grabar N, Hamon T, Dart T. Term variation and semantics for document classification and detection of obesity and its Co-morbidities cases. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  22. Guillen R. Identifying obesity and co-morbidities from medical records. Proceedings of the i2b2 Workshop on challenges in natural language processing for clinical data, 2008.
  23. Hara K. Classifying narrative patient records without any external resources. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  24. Harkema H, Piwowar H, Amizadeh S, Dowling J, Ferraro J, Haug P, Chapman W. A baseline system for the i2b2 obesity challenge. Proceedings of the i2b2 Work- shop on Challenges in Natural Language Processing for Clinical Data, 2008.
  25. Ho B, Nytrø Ø, Bassøe CF. NLP obesity challenge: Using clinical markers for EHR classification. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  26. MacNamee B, Kelleher JD, Delany SJ. Medical language processing for patient diagnosis using text classification and negation labeling. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  27. Mata J, Maña MJ, Bermúdez JM, Cruz NP, Jiménez P. Handling negation in classification of clinical texts. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  28. Matthews MP. Bayesian networks and the i2b2 obesity challenge. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  29. McCormick PJ, Elhadad N, Stetson P. Use of semantic features to classify patient smoking status. Proceedings of AMIA Annual Symposium 2008; pp. 450-454.
  30. McInnes BT. Using Cui Tools to identify obesity and its Co-morbidities in discharge summaries. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  31. Meystre SM. Detecting patients suffering from obesity and common comorbidities by analyzing narrative clinical text. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  32. Neves M, Carazo JM, Pascual-Montano A. Botero: A SVM classifier for clinical text in the obesity domain. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  33. Patrick J, Asgari P. A brief summary about the approach and explanation of the attributes of the developed system for i2b2 challenge. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  34. Pedersen T. Learning high precision rules to make predictions of morbidities in discharge summaries. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  35. Peshkin L, Cano C, Carpenter B, Baldwin B. Regularized logistic regression for clinical record processing. Proceedings of the i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2008.
  36. Savova G, Clark C, Zheng J, Cohen KB, Murphy S, Wellner B, Harris D, Lazo M, Aberdeen J, Hu Q, Chute C, Hirschman L. The MAYO/MITRE system for discovery of obesity and its comorbidities. Proceedings of the i2b2 Workshop on challenges in natural language processing for clinical data, 2008.
  37. Uzuner Ö, Goldstein I, Luo Y, Kohane I.  Identifying patient smoking status from medical discharge records. JAMIA 2008; 15(1):14-24.
  38. Wicentowski R, Sydes MR. Using implicit information to identify smoking status in smoke-blind medical discharge summaries. JAMIA 2008; 15(1):29-31; doi: 10.1197/jamia.M2440.
  39. Heinze DT, Morsch ML, Potter BC, Sheffer RE. A-life medical i2b2 NLP smoking challenge system architecture and methodology. JAMIA 2008; 15(1):40-3; doi: 10.1197/jamia.M2438.
  40. Ambert KH, Cohen AM. A system for classifying disease comorbidity status from medical discharge summaries using automated hotspot and negated concept detection. JAMIA 2009;16:590–5.
  41. Bhattarai A, Rus V, Dashupta D. Classification of clinical conditions: a case study on prediction of obesity and its comorbidities. Proceedings 10th international conference on Intelligent Text Processing and Computational Linguistics. March 1-7, Mexico City, Mexico.
  42. Childs L, Enelow R, Simonsen L, Heintzelman NH, Kowalski KM, Taylor RJ. Description of a rule-based system for the i2b2 challenge in natural language processing for clinical data. JAMIA 2009; 16:571-5; doi:10.1197/jamia.M3083.
  43. Doan S, Xu H. Recognizing medication related entities in hospital discharge summaries using support vector machine. Proceedings of the 23rd International Conference on Computational Linguistics, COLING ‘10: Posters.
  44. Farkas R, Szarvas G, Hegedus I, Almasi A, Vincze V, Ormandi R, Busa-Fekete R. Semi-automated construction of decision rules to predict morbidities from clinical texts. JAMIA 2009; 16:601-5;  doi:10.1197/jamia.M3097.
  45. Hamon T, Grabar N. Concurrent linguistic annotations for identifying medication names and the related information in discharge summaries. Proceedings of the Third i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2009.
  46. Li Z, Cao Y, Antineau L, Agarwal S, Yu H. Extracting medication information from patient discharge summaries. Proceedings of the Third i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2009.
  47. Meystre S. Detecting intuitive mentions of diseases in narrative clinical text. Artificial Intelligence in Medicine. Lecture Notes in Computer Science 2009; 5651:216-224; doi:10.1007/978-3-642-02976-9_31.
  48. Mishra N, Cummo D, Arnzen J, Bonander J. A rule-based approach for identifying obesity and its comorbidities in medical discharge summaries. JAMIA 2009; 16:576-9; doi:10.1197/jamia.M3086.
  49. Solt I. Automatic semantic annotation of medical discharge summaries. Available at: http://152.66.244.218/cgi-bin/demo.pl. Accessed: Apr 2, 2009.
  50. Solt I, Tikk D, Gál V, Kardkovács ZT. Semantic classification of diseases in discharge summaries using a context-aware rule-based classifier. JAMIA 2009;16:580 – 4.
  51. Teuchert AR, Tabet JS, DuVall SL. System description. VA Salt Lake City Health Care System. Proceedings of the Third i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, 2009.
  52. Uzuner O. Recognizing obesity and comorbidities in sparse data. JAMIA 2009; Published Online First 1 July 2009; 16(4):561-60; doi:10.1197/jamia.M3115.
  53. Ware H, Mullett CJ, Jagannathan V. Natural language processing framework to assess clinical conditions. JAMIA 2009:585–9.
  54. Yang H, Spasic I, Keane JA, Nenadic G. A text mining approach to the prediction of a disease status from clinical discharge summaries. JAMIA 2009;16:596–600.
  55. Abacha AB, Zweigenbaum P. Automatic extraction of semantic relations between medical entities: a rule based approach. Proceedings of the fourth International Symposium on Semantic Mining in Biomedicine 2010, Hinxton, UK, October 2010.
  56. Anick P, Hong P, Xue N, Xue N, Anick D. I2B2 2010 challenge: machine learning for information extraction from patient records. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  57. Arnold C, Speier W, Hsu Wm Garcia-Gathright J, Wu J, Tong M, Taira R. UCLA summary for i2b2/VA 2010 NLP shared-task challenge. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010
  58. Chang E, Xu Y, Hong K, Dong J, Gu Z. A hybrid approach to extract structured information from narrative clinical discharge summaries. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  59. Cohen AM, Ambert K, Yang J, Felder R, Sproat R, Roark B, Hollingshead K, Balker K. OHSU/Portland VAMC team participation in the 2010 i2b2/VA challenge tasks. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  60. Deleger L, Grouin C, Zweigenbaum P. Extracting medical information from narrative patient records: the case of medication-related information. JAMIA 2010; 17(5):555-8; doi:10.1136/jamia.2010.003962.
  61. Demner-Fushman D, Apostolova E, Islamaj Dogan R, Lang FM, Mork J, Neveol A, Shooshan S, Simpson M, Aronson A. NLM’s system description for the fourth i2b2/VA challenge. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  62. Divita G, Treitler OZ, Kim YJ, Redd D, Meystre S, Kandula S, Gundlapalli A. Salt Lake City VA’s challenge submissions. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  63. Doan S, Bastarache L, Klimkowski S. Integrating existing natural language processing tools for medication extraction from discharge summaries. JAMIA 2010; 17(5):528-31; doi:10.1136/jamia.2010.003855.
  64. Fan JW, Huang Y, Yabut R, Zisook D, Mattison J. Automated extraction of clinical concepts- an i2b2 experience. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  65. Frunza D, Inkpen D. Identifying and classifying semantic relations between medical concepts in clinical data (i2b2 challenge). Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  66. Galescu L, Blaylock N, Allen J, Beaumont W, Jung H, Swift M. A deep NLP system for extracting knowledge from clinical text: application to the i2b2/VA concept extraction task. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  67. Guillen R, Cowart C. Concept extraction, concept semantic characterization and assertion classification from clinical data. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  68. Gurulingappa H, Hofmann-Apitius M, Fluck J. Concept identification and assertion classification in patient health records. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  69. Halgrim S, Xia F, Solti I, Cadag E, Uzuner O. Statistical extraction of medication information from clinical records. Proceedings of 2010 AMIA Summit on Translational Bioinformatics, San Francisco, CA, March 10-12, 2010.
  70. Halgrim S, Xia Fei, Solti I, Cadag E, Uzuner O. Extracting medication information from discharge summaries. Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents (Louhi ’10). Association for Computational Linguistics, Stroudsburg, PA, USA, 61-67.
  71. Hamon T, Perinet A Nobecourt J, Grabar N. Linguistic and semantic annotation for information extraction characterization. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  72. Hina S, Atwell E, Johnson O, West R. Extracting the concepts in clinical documents using SNOMED-CT and GATE. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  73. Kang N, Barendse RJ, Afzal Z, Singh B, Schumenie M, Mulligen EM, Kors J. Erasmus MC approaches to the i2b2 Challenge. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  74. Jonnalagadda S, Gonzalez G. Can distributional statistics aid clinical concept extraction? Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  75. Li Z, Liu F, Antieau L, Cao Y, Yu H. Lancet: a high precision medication event extraction system for clinical text. JAMIA 2010; 17(5):563-7; doi:10.1136/jamia.2010.004077.
  76. Meystre S, Thibault J, Shen S, Hurdle J, South BR. Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents. JAMIA 2010; 17(5):559-62; doi:10.1136/jamia.2010.004028.
  77. Mork JG, Bodenreider O, Demner-Fushman D,  Dogan DI, Lang FM, Luo Z, Neveol A, Peters L, Shooshan S, Aronson A. Extracting Rx information from clinical narrative. JAMIA 2010; 17(5):536-9; doi:10.1136/jamia.2010.003970.
  78. Pai AK, Agichtein E, Post AR, Saltz J. The Emory system for extracting medical concepts at 2010 i2b2 challenge: integrating natural language processing and machine learning techniques. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  79. Patrick J, Li M. High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. JAMIA 2010; 17(5):524-7; doi:10.1136/jamia.2010.003939.
  80. Patrick JD, Asgari P, Motamedi N. Identifying clinical concepts in unstructured clinical notes using existing knowledge within the corpus. Proceedings of 23rd IEEE International Symposium on Computer-Based Medical Systems (CBMS ’10); doi:10.1109/CBMS.2010.6042616.
  81. Rosales R, Faroow F, Krishnapuram B, Yu S, Fung G. Automated identification of medical concepts and assertions in medical text. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  82. Rost T, Akbar S, Nytro O, Basgalupp M. Medical relation extraction with semantic grammars. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  83. Sasaki Y, Ishihara K, Yamamoto Y, Weissenbacher D. TTI’s systems for 2010 i2b2/VA challenge. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  84. Sohn S, Murphy S, Masanz J, Kaggal V, Zheng J. Rule-based assertion classification of medical problems in clinical narratives. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  85. Solt I, Szidarovszky FP, Tikk D. Concept, assertion and relation extraction at the 2010 i2b2 relation extraction challenge using parsing information and dictionaries. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  86. Spasic I, Sarafraz F, Keane JA Nenadic G. Medication information extraction with linguistic pattern matching and semantic rules. JAMIA 2010; 17(5):532-5; doi:10.1136/jamia.2010.003657.
  87. Sue YK, Nguyen A, Sitbon L, Geva S. Rule-based approach for identifying assertions in clinical free-text data. Proceedings of the ACDS conference, Melbourne, Australia, 2010.
  88. Sun Y, Nguyen A, Geva S, Sitbon L. Rule-based applications for identifying assertions in clinical free-text data. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  89. Swift M, Blaylock N, Allen J, deBeaumont W, Galescu L, Jung H. Augmenting a deep natural language processing system with UMLS. Proceedings of the fourth International Symposium on Semantic Mining in Biomedicine 2010, Hinxton, UK, October 2010.
  90. Tikk D, Solt I. Improving textual medication extraction using combined conditional random fields and rule-based systems. JAMIA 2010; 17(5):540-4; doi:10.1136/jamia.2010.004119.
  91. Torii M, Liu H. BioTagger-GM for detecting clinical concepts in electronic medical reports. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010.
  92. Uzuner O, Solti I, Xia F, Cadag E. Community annotation experiment for ground truth generation for the i2b2 medication challenge. JAMIA 2010; 17(5):519-23; doi:10.1136/jamia.2010.004200.
  93. Uzuner Ö, Solti I, Cadag E. Extracting medication information from clinical text. JAMIA 2010;17:514-518 doi:10.1136/jamia.2010.003947.
  94. Ware H, Mullet C, Jagannathan V, El-Rawas O. Natural language processing framework to abstract problems, treatments, and tests from clinical documents. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  95. Yang H. Automatic extraction of medication information from medical discharge summaries. JAMIA 2011; 17(5):545-8; doi:10.1136/jamia.2010.003863.
  96. Yang H, deRoeck A. Extracting of medical information using CRFs, context patterns, and dependency parse trees. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  97. Yetisgen-Yildiz M, Saleem S, Capurro D. A hybrid system for named-entity extraction from clinical discharge summaries. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2 2010.
  98. Abacha AB, Zweigenbaum P. Medical entity recognition: a comparison of semantic and statistical methods. Proceedings of the 10th ACL workshop on Biomedical Natural Language Processing (BioNLP’11), Portland, Oregon, USA, 2011.
  99. Chapman W, Nadkarni P, 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. JAMIA 2011; 18:540-3; doi:10.1136/amiajnl-2011-000465.
  100. Clark C, Aberdeen J, Coarr M, Tresner-Kirsch D, Wellner B, Yeh A, Hirschman L. MITRE system for clinical assertion status classification. JAMIA 2011;Published Online First: 22 April 2011; 18(5):563-7; doi:10.1136/amiajnl-2011-000164.
  101. D'Avolio L, Nguyen TM,  Goryachev S,  Fiore LD.  Automated concept-level information extraction to reduce the need for custom software and rules development. JAMIA 2011;Published Online First: 22 June 2011; 18:607-13; doi:10.1136/amiajnl-2011-000183.
  102. deBruijn B, Cherry C, Kiritchenko S, Martin J, Zhu X.  Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. JAMIA 2011;Published Online First: 12 May 2011; 18(5):557-562; doi:10.1136/amiajnl-2011-000150.
  103. Frunza O, Inkpen D. Extracting relations between diseases, treatments, and tests from clinical data. Proceedings of the 24th Canadian conference on Artificial Intelligence, St John, NB, May 2011.
  104. Halgrim SR, Xia F, Solti I, Cadag E, Uzuner O. A cascade of classifiers for extracting medication information from discharge summaries. Journal of Biomedical Semantics; Published Online 2011 July; 2(3):S2; doi:10.1186/2041-1480-2-S3-S2.
  105. Kim Y, Riloff E, Meystre SM. Improving classification of medical assertions in clinical notes. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2 (HLT '11), Vol. 2. Association for Computational Linguistics, Stroudsburg, PA, USA, 311-316.
  106. Jiang M, Chen Y, Liu M, Rosenbloom ST, Mani S, Denny JC, Xu H. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. JAMIA 2011; Published Online First 20 April 2011; 18(5): 601-6; doi:10.1136/amiajnl-2011-000163.
  107. Jonnalagadda S, Cohen T, Wu S, Gonzalez G. Enhancing clinical concept extraction with distributional semantics. Journal of Biomedical Informatics 2011; Epub ahead of print; doi:10.1016/j.jbi.2011.10.007.
  108. Minard AL, Ligozat AL, Abacha AB, Bernhard D, Cartoni B, Deléger L, Grau B, Rosset S, Zweigenbaum P, Grouin C. Hybrid methods for improving information access in clinical documents: concept, assertion, and relation identification. JAMIA 2011;Published Online First: 19 May 2011; 18(5):588-93; doi:10.1136/amiajnl-2011-000154.
  109. Patrick JD, Nguyen DHM, Wang Y, Min Li. A knowledge discovery and reuse pipeline for information extraction in clinical notes. JAMIA 2011; Published Online First: 7 July 2011; 18(5):574-9; doi:10.1136/amiajnl-2011-000302.
  110. Rink B, Harabagiu SM, Roberts K. Automatic extraction of relations between medical concepts in clinical texts. JAMIA 2011; 18(5):594-600; doi:10.1136/amiajnl-2011-000153.
  111. Rink B, Harabagiu S. A generative model for unsupervised discovery of relations and argument classes from clinical texts. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Edinburgh, UK, 519-528.
  112. Roberts K, Harabagiu SM. A flexible framework for deriving assertions from electronic medical records. JAMIA 20; 18:574-579 Published Online First: 7 July 2011 doi:10.1136/amiajnl-2011-000302.
  113. Ryan R. Groundtruth budgeting: Weakly-supervised relation extraction of medical language. MIT 2011 Master’s Thesis.
  114. Torii M, Wagholikar K, Liu H. Using machine learning for concept extraction on clinical documents from multiple data sources. JAMIA 2011; Published Online First 27 June 2011; 18:580-7; doi:10.1136/amiajnl-2011-000155.
  115. Uzuner O, South BR, Shen S, DuVall SL. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. JAMIA 2011; Published Online First: 16 June 2011; 18(5):552-6; doi:10.1136/amiajnl-2011-000203.
  116. Dai H, Chen C, Wu C, Lai P, Tsai RT, Hsu W. Coreference resolution of medical concepts in discharge summaries by exploiting contextual information. JAMIA Published Online First: 3 May 2012 doi:10.1136/amiajnl-2012-000808
  117. Gooch P, Roudsari A. Lexical patterns, features and knowledge resources for coreference resolution in clinical notes. Journal of Biomedical Informatics. http://dx.doi.org/10.1016/j.jbi.2012.02.012.
  118. Jindal P, Roth D. Using domain knowledge and domain-inspired discourse model for coreference resolution for clinical narratives. J Am Med Inform Assoc amiajnl-2011-000767Published Online First: 10 July 2012 doi:10.1136/amiajnl-2011-000767.
  119. Jonnalagadda SR, Li D, Sohn S., Wu ST, Wagholikar K, Torii M, Liu H. Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules. JAMIA Published Online First: 16 June 2012 doi:10.1136/amiajnl-2011-000766.
  120. Rink B, Roberts K, Harabagiu SM. A supervised framework for resolving coreference in clinical records. JAMIA Published Online First: 19 May 2012 doi:10.1136/amiajnl-2012-000810.
  121. Uzuner Ö, Bodnari A, Shen S, Forbush T, Pestian J, South BR. Evaluating the state of the art in coreference resolution for electronic medical records. J Am Med Inform Assoc 2012;19:786-791 Published Online First: 24 February 2012 doi:10.1136/amiajnl-2011-000784.
  122. Ware W, Mullett CJ, Jagannathan V, El-Rawas O. Machine learning-based coreference resolution of concepts in clinical documents. JAMIA Published Online First: 12 May 2012 doi:10.1136/amiajnl-2011-000774.
  123. Xu Y, Hong K, Tsujii J, Chang EI. Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries. JAMIA Published Online First: 14 May 2012 doi:10.1136/amiajnl-2011-000776i.
  124. Xu Y, Liu J, Wu J, Wang Y, Tu Z, Sun J, Tsujii J, Chang EI. A classification approach to coreference in discharge summaries: 2011 i2b2 challenge. JAMIA Published Online First: 13 April 2012 doi:10.1136/amiajnl-2011-000734.

 
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