Text mining the history of medicine

PLoS ONE

Volumn 11, Issue 1, 2016, Pages

  Thompson, Paul ^a   Batista Navarro, Riza Theresa ^a   Kontonatsios, Georgios ^a   Carter, Jacob ^a   Toon, Elizabeth ^a   McNaught, John ^a   Timmermann, Carsten ^a   Worboys, Michael ^a   Ananiadou, Sophia ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DATA MINING; HISTORY OF MEDICINE; INFORMATION PROCESSING; MEDICAL DOCUMENTATION; MEDICAL HISTORY; MEDICAL INFORMATICS; MEDICAL INFORMATION SYSTEM; MEDICAL RECORD; MEDICAL TERMINOLOGY; TEXT MINING; HISTORY; SEMANTICS;

DATA MINING; HISTORY OF MEDICINE; HISTORY, 19TH CENTURY; SEMANTICS;

EID: 84954055651     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0144717     Document Type: Article

References (84)

1. Worboys M. Spreading germs: disease theories and medical practice in Britain, 1865-1900: Cambridge University Press; 2000.
2. Roberts A, Gaizauskas R, Hepple M, Demetriou G, Guo Y, Setzer A, et al. Semantic annotation of clinical text: The CLEF corpus. In: Proceedings of the LREC 2008 workshop on building and evaluating resources for biomedical text mining; 2008. p. 19-26.
3. Uzuner Ö. Recognizing obesity and comorbidities in sparse data. J Am Med Inform Assoc. 2009;16(4):561-70. doi: 10.1197/jamia. M3115 PMID: 19390096
4. Uzuner Ö, Goldstein I, Luo Y, Kohane I. Identifying patient smoking status from medical discharge records. J Med Inform Assoc. 2008;15(1):14-24.
5. Uzuner Ö, South BR, Shen S, DuVall SL. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc. 2011;18(5):552-6. doi: 10.1136/amiajnl-2011-000203 PMID: 21685143
6. Suominen H, Salanterä S, Velupillai S, Chapman WW, Savova G, Elhadad N, et al. Overview of the ShARe/CLEF eHealth Evaluation Lab 2013. Information Access Evaluation: Multilinguality, Multimodality, and Visualization. 8138: Springer; 2013. p. 212-31.
7. Kelly L, Goeuriot L, Suominen H, Schreck T, Leroy G, Mowery DL, et al. Overview of the ShARe/CLEF eHealth Evaluation Lab 2014. Information Access Evaluation: Multilinguality, Multimodality, and Interaction. 8685: Springer; 2014. p. 172-91.
8. Rodriquez KJ, Bryant M, Blanke T, Luszczynska M. Comparison of named entity recognition tools for raw OCR text. In: Proceedings of KONVENS (LThist 2012 workshop); 2012. p. 410-4.
9. Tsuruoka Y, Tateishi Y, Kim JD, Ohta T, McNaught J, Ananiadou S, et al. Developing a Robust Part-of-Speech Tagger for Biomedical Text. Lecture Notes in Computer Science-Advances in Informatics-10th Panhellenic Conference on Informatics. 3746: Springer; 2005. p. 382-92.
10. Hara T, Miyao Y, Tsujii J. Adapting a probabilistic disambiguation model of an HPSG parser to a new domain. In: Proceedings of IJCNLP; 2005. p. 199-210.
11. Sanchez-Marco C, Boleda G, Padro L. Extending the tool, or how to annotate historical language varieties. In: Proceedings of the 5th ACL-HLT workshop on language technology for cultural heritage, social sciences, and humanities; 2011. p. 1-9.
12. Dipper S. POS-tagging of historical language data: First experiments. In: Proceedings of the 10th Conference on Natural Language Processing (KONVENS 2010);2010. p. 117-21.
13. Schneider G. Adapting a parser to historical English. In: Tyrkkö J, Kilpiö M, Nevalainen T, Rissanen M, editors. Studies in Variation, Contacts and Change in English-Outposts of Historical Corpus Linguistics: From the Helsinki Corpus to a Proliferation of Resources. 10. Helsinki: Research Unit for Variation, Contacts and Change in English (VARIENG), University of Helsinki; 2012.
14. Burns PR. MorphAdorner v2: a Java Library for the morphological adornment of English language texts 2013. Available: https://morphadorner.northwestern. edu/morphadorner/download/morphadorner.pdf.
15. Baron A, Rayson P. VARD2: A tool for dealing with spelling variation in historical corpora. In: Proceedings of the Postgraduate conference in corpus linguistics; 2008.
16. Bollmann M. POS tagging for historical texts with sparse training data. In: Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse; 2013. p. 11-18.
17. Kumar A. Monk project: Architecture overview. In: Proceedings of JCDL 2009 Workshop: Integrating Digital Library Content with Computational Tools and Services; 2009.
18. Furrer L, Volk M. Reducing OCR errors in Gothic-script documents. In: Proceedings of the RANLP 2011 workshop on Language Technologies for Digital Humanities and Cultural Heritage; 2011. p. 97-103.
19. Lopresti D. Optical character recognition errors and their effects on natural language processing. Int J Doc Anal Recognit. 2009;12(3):141-51.
20. Miller D, Boisen S, Schwartz R, Stone R, Weischedel R. Named entity extraction from noisy input: speech and OCR. In: Proceedings of the Sixth Conference on Applied Natural Language Processing; 2000. p. 316-24.
21. Grover C, Givon S, Tobin R, Ball J. Named Entity Recognition for Digitised Historical Texts. In: Proceedings of LREC; 2008. p. 1343-6.
22. Bontcheva K, Maynard D, Cunningham H, Saggion H. Using human language technology for automatic annotation and indexing of digital library content. Research and Advanced Technology for Digital Libraries. 2458: Springer; 2002. p. 613-25.
23. Alex B, Grover C, Klein E, Tobin R. Digitised historical text: Does it have to be mediOCRe. In: Proceedings of KONVENS; 2012. p. 401-9.
24. Packer TL, Lutes JF, Stewart AP, Embley DW, Ringger EK, Seppi KD, et al. Extracting person names from diverse and noisy OCR text. In: Proceedings of the fourth workshop on Analytics for noisy unstructured text data; 2010. p. 19-26.
25. Borin L, Kokkinakis D, Olsson L-J. Naming the past: Named entity and animacy recognition in 19th century Swedish literature. In: Proceedings of the Workshop on Language Technology for Cultural Heritage Data (LaTeCH 2007);2007. p. 1-8.
26. Crane G, Jones A. The challenge of virginia banks: an evaluation of named entity analysis in a 19thcentury newspaper collection. In: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries; 2006. p. 31-40.
27. Klein E, Alex B, Clifford J. Bootstrapping a historical commodities lexicon with SKOS and DBpedia. In: Proceedings of the EACL LaTeCH Workshop; 2014. p. 13-21.
28. Grover C, Tobin R. A Gazetteer and Georeferencing for Historical English Documents. In: Proceedings of the EACL LaTeCH Workshop; 2014. p. 119-27.
29. Rupp C, Rayson P, Baron A, Donaldson C, Gregory I, Hardie A, et al. Customising geoparsing and georeferencing for historical texts. In: Proceedings of the IEEE International Conference on Big Data; 2013. p. 59-62.
30. Klein E, Alex B, Grover C, Tobin R, Coates C, Clifford J, et al. Digging Into Data White Paper: Trading Consequences. 2014.
31. Rupp C, Rayson P, Gregory I, Hardie A, Joulain A, Hartmann D. Dealing with heterogeneous big data when geoparsing historical corpora. In: Proceedings of the IEEE International Conference on Big Data; 2014. p. 80-3.
32. Hinrichs U, Alex B, Clifford J, Quigley A. Trading Consequences: A Case Study of Combining Text Mining & Visualisation to Facilitate Document Exploration. In: Proceedings of DH 2014;2014.
33. Hitchcock T, Turkel WJ, Helmreich A. Rethinking inventories in the digital age: the case of the Old Bailey. Journal of Art Historiography. 2014;11.
34. Murrieta-Flores P, Baron A, Gregory I, Hardie A, Rayson P. Automatically Analyzing Large Texts in a GIS Environment: The Registrar General's Reports and Cholera in the 19th Century. Trans GIS. 2014;19(2):296-320.
35. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004;32:267-70.
36. Schriml LM, Arze C, Nadendla S, Chang Y-WW, Mazaitis M, Felix V, et al. Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res. 2012;40(D1):D940-D6.
37. Tsuruoka Y, McNaught J, Ananiadou S. Normalizing biomedical terms by minimizing ambiguity and variability. BMC Bioinformatics. 2008;9 Suppl 3:S2. doi: 10.1186/1471-2105-9-S3-S2 PMID: 18426547
38. Tsuruoka Y, McNaught J, Tsujii J, Ananiadou S. Learning string similarity measures for gene/protein name dictionary look-up using logistic regression. Bioinformatics. 2007;23(20):2768-74. PMID: 17698493.
39. Aronson AR, Lang F-M. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc. 2010;17(3):229-36. doi: 10.1136/jamia.2009.002733 PMID: 20442139
40. Hearst MA. Automated discovery of WordNet relations. WordNet: an electronic lexical database. Cambridge, MA: MIT Press; 1998. p. 131-53.
41. Ruiz-Casado M, Alfonseca E, Castells P. Automatic extraction of semantic relationships for wordnet by means of pattern learning from wikipedia. Natural Language Processing and Information Systems. 3513: Springer; 2005. p. 67-79.
42. Harris ZS. Distributional structure. Word. 1954;10(23):146-62.
43. Firth JR. A synopsis of linguistic theory, 1930-1955. In: Palmer FR, editor. Selected papers of JR Firth 1952-9. Harlow: Longman; 1957.
44. Lin D. Automatic retrieval and clustering of similar words. In: Proceedings of ACL; 1998. p. 768-74.
45. Broda B, Derwojedowa M, Piasecki M, Szpakowicz S. Corpus-based Semantic Relatedness for the Construction of Polish WordNet. In: Proceedings of LREC; 2008. p. 1800-7.
46. Karan M, šnajder J, Bašić BD. Distributional semantics approach to detecting synonyms in Croatian language. In: Proceedings of the Eighth Language Technologies Conference; 2012. p. 111-6.
47. McCarthy D, Koeling R, Weeds J, Carroll J. Finding predominant word senses in untagged text. In: Proceedings of ACL; 2004. p. 280-7.
48. Jonnalagadda S, Cohen T, Wu S, Liu H, Gonzalez G. Using empirically constructed lexical resources for named entity recognition. Biomed Inform Insights. 2013;6(Suppl 1):17. doi: 10.4137/BII. S11664 PMID: 23847424
49. Moen H, Ginter F, Marsi E, Peltonen L-M, Salakoski T, Salanterä S. Care episode retrieval: distributional semantic models for information retrieval in the clinical domain. BMC Med Inform Decis Mak. 2015;15(Suppl 2):S2. doi: 10.1186/1472-6947-15-S2-S2 PMID: 26099735
50. Zhang S, Elhadad N. Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts. J Biomed Inform. 2013;46(6):1088-98. doi: 10.1016/j.jbi.2013.08.004 PMID: 23954592
51. Henriksson A, Moen H, Skeppstedt M, Daudaravicius V, Duneld M. Synonym extraction and abbreviation expansion with ensembles of semantic spaces. J Biomed Semantics. 2014;5(6).
52. Tahmasebi N, Gossen G, Kanhabua N, Holzmann H, Risse T. NEER: An Unsupervised Method for Named Entity Evolution Recognition. In: Proccedings of COLING; 2012. p. 2553-68.
53. Berberich K, Bedathur SJ, Sozio M, Weikum G. Bridging the Terminology Gap in Web Archive Search. In: Proceedings of WebDB; 2009.
54. Thompson P, McNaught J, Ananiadou S. Customised OCR Correction for Historical Medical Text. In: Proceedings of DigitalHeritage; 2015.
55. Wagholikar KB, Torii M, Jonnalagadda S, Liu H. Pooling annotated corpora for clinical concept extraction. J Biomed Semantics. 2013;4:3. doi: 10.1186/2041-1480-4-3 PMID: 23294871
56. Wang Y, Pakhomov S, Ryan JO, Melton GB. Domain adaption of parsing for operative notes. J Biomed Inform. 2015;54:1-9. doi: 10.1016/j.jbi.2015.01.016 PMID: 25661593
57. Alnazzawi N, Thompson P, Ananiadou S. Building a semantically annotated corpus for congestive heart and renal failure from clinical records and the literature. In: Proceedings of the EACL Louhi Workshop; 2014. p. 69-74.
58. Alnazzawi N, Thompson P, Batista-Navarro R, Ananiadou S. Using text mining techniques to extract phenotypic information from the PhenoCHF corpus. BMC Med Inform Decis Mak. 2015;15(Suppl 2):S3. doi: 10.1186/1472-6947-15-S2-S3 PMID: 26099853
59. Pyysalo S, Ananiadou S. Anatomical entity mention recognition at literature scale. Bioinformatics. 2014;30(6):868-75. doi: 10.1093/bioinformatics/btt580 PMID: 24162468
60. Mihãilã C, Ohta T, Pyysalo S, Ananiadou S. BioCause: Annotating and analysing causality in the biomedical domain. BMC Bioinformatics. 2013;14:2. doi: 10.1186/1471-2105-14-2 PMID: 23323613
61. Prasad R, McRoy S, Frid N, Joshi A, Yu H. The biomedical discourse relation bank. BMC Bioinformatics. 2011;12:188. doi: 10.1186/1471-2105-12-188 PMID: 21605399
62. Kim J-D, Ohta T, Tsujii J. Corpus annotation for mining biomedical events from literature. BMC Bioinformatics. 2008;9(10).
63. Miwa M, Saetre R, Kim JD, Tsujii J. Event extraction with complex event classification using rich features. J Bioinform Comput Biol. 2010;8(1):131-46. PMID: 20183879
64. Björne J, Ginter F, Heimonen J, Pyysalo S, Salakoski T. Learning to Extract Biological Event and Relation Graphs. In: Proceedings of NODALIDA'09, Odense, Denmark; 2009. p. 18-25.
65. McClosky D, Riedel S, Surdeanu M, McCallum A, Manning C. Combining joint models for biomedical event extraction. BMC Bioinformatics. 2012;13(Suppl 11):S9. doi: 10.1186/1471-2105-13-S11-S9 PMID: 22759463
66. Stenetorp P, Pyysalo S, Topić G, Ohta T, Ananiadou S, Tsujii J. brat: a Web-based Tool for NLP-Assisted Text Annotation. In: Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics; 2012. p. 102-7.
67. Hripcsak G, Rothschild AS. Agreement, the f-measure, and reliability in information retrieval. J Am Med Inform Assoc. 2005;12(3):296-8. PMID: 15684123
68. Thompson P, Iqbal S, McNaught J, Ananiadou S. Construction of an annotated corpus to support biomedical information extraction. BMC Bioinformatics. 2009;10(1):349.
69. Aronson AR, Rindflesch TC. Query expansion using the UMLS Metathesaurus. In: Proceedings of the AMIA Annual Fall Symposium; 1997. p. 485-9.
70. Hersh W, Price S, Donohoe L. Assessing thesaurus-based query expansion using the UMLS Metathesaurus. In: Proceedings of the AMIA Symposium; 2000. p. 344-8.
71. Liu Z, Chu WW. Knowledge-based query expansion to support scenario-specific retrieval of medical free text. Inf Retr Boston. 2007;10(2):173-202.
72. Tanner TH, Broadbent WH. Index of diseases and their treatment. Philadelphia: Blakiston; 1882.
73. American Medical Association Committee on Nomenclature. A nomenclature of diseases: with the reports of the majority and of the minority of the committee thereon: presented to the American Medical Association at the meeting held in Philadelphia, May 1872. Phildeliphia: Collins; 1872.
74. Schmid H. Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the International Conference on New Methods in Language Processing; 1994. p. 44-9.
75. Mitchell J, Lapata M. Vector-based Models of Semantic Composition. In: Proceedings of ACL; 2008. p. 236-44.
76. Kaewphan S, Hakaka K, Ginter F. UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations. In: Proceeedings of SemEval; 2014. p. 807-11.
77. Ohta T, Pyysalo S, Tsujii J, Ananiadou S. Open-domain Anatomical Entity Mention Detection. In: Proceedings of the ACL Workshop on Detecting Stucture in Scholarly Discourse (DSSD); 2012. p. 27-36.
78. Pradhan S, Elhadad Nm, Chapman W, Manandhar S, Savova G. SemEval-2014 Task 7: Analysis of Clinical Text. In: Proceedings of SemEval; 2014. p. 54-62.
79. Miwa M, Thompson P, Ananiadou S. Boosting automatic event extraction from the literature using domain adaptation and coreference resolution. Bioinformatics. 2012;28(13):1759-65. doi: 10.1093/bioinformatics/bts237 PMID: 22539668
80. Miwa M, Ananiadou S. NaCTeM EventMine for BioNLP 2013 CG and PC tasks. In: Proceedings of BioNLP 2013;2013. p. 94-8.
81. Miwa M, Thompson P, Korkontzelos I, Ananiadou S. Comparable Study of Event Extraction in Newswire and Biomedical Domains. In: Proceedings of COLING; 2014. p. 2270-9.
82. Dogan RI, Leaman R, Lu Z. NCBI disease corpus: a resource for disease name recognition and concept normalization. J Biomed Inform. 2014;47:1-10. doi: 10.1016/j.jbi.2013.12.006 PMID: 24393765
83. Cohen J. A coefficient of agreement for nominal scales. Educational and psychological measurement. 1960;20(1):37-46.
84. Thompson P, Carter J, McNaught J, Ananiadou S. Semantically Enhanced Search System for Historical Medical Archives In: Proceedings of DigitalHeritage; 2015.