LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Zhao, Bo (2016)
Languages: English
Types: Doctoral thesis
Subjects: QA75
In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities.\ud In this thesis, we describe KneeTex, an information extraction system that operates in this domain. As an ontology-driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain-specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexicosemantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00%, recall of 97.63% and F-measure of 97.81%, the values of which are in line with human-like performance.\ud To demonstrate the utility of formally structuring clinical narratives and possible applications in epidemiology, we describe an implementation of KneeBase, a web-based information retrieval system that supports complex searches over the results obtained via KneeTex. It is the structured nature of extracted information that allows queries that encode not only search terms, but also relationships between them (e.g. between clinical findings and anatomical locations). This is of particular value for large-scale epidemiology studies based on qualitative evidence, whose main bottleneck involves manual inspection of many text documents.\ud The two systems presented in this dissertation, KneeTex and KneeBase, operate in a specific domain, but illustrate generic principles for rapid development of clinical text mining systems. The key enabler of such systems is the existence of an appropriate ontology. To tackle this issue, we proposed a strategy for ontology expansion, which proved effective in fast–tracking the development of our information extraction and retrieval systems.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 8.1 Summary of contributions .................................................................................................99
    • Table 2-1 Part-of-speech frequency distributions in clinical and non-clinical english texts (Campbell and S. B. Johnson, 2001).....................................................................................9
    • Table 2-2 Part-of-speech bigram frequency distributions in clinical and non-clinical english texts (Campbell and S. B. Johnson, 2001).....................................................................................9
    • Table 2-3 Yearly i2b2 shared task challenges (Uzuner, 2009; Uzuner et al., 2006; 2008; 2007; 2010; 2011; Uzuner and Stubbs, 2015)...............................................................................15
    • Table 4-1 Comparison of diagnostic values for meniscus tear with and without MRI (Yan et al., 2011) ...................................................................................................................................39
    • Table 4-2 Training set statistics ...................................................................................................42
    • Table 4-3 Top 20% frequently occurred semantic types .............................................................44
    • Table 4-4 Classification of semantic types ..................................................................................45
    • Table 4-5 Semantic type classifications to annotation tags conversion interpretation and examples .............................................................................................................................................46
    • Table 4-6 MetaMap performances on development set (Exact match) .......................................47
    • Table 4-7 Fleiss' Kappa coefficient value interpretation (Landis and Koch, 1977) ....................48
    • Table 5-1 Statistics of annotated terms on development set........................................................58
    • Table 6-1 An excerpt of conversion from ontology vocabulary to PathNER dictionary ............69
    • Table 6-2 Corresponding semantic types for slots.......................................................................81
    • Table 6-3 System performances on test set over slots .................................................................86
    • Figure 2-1 A template for medical records information extraction .............................................11
    • Figure 2-2 A common information extraction system structure (Piskorski and Yangarber, 2013) .............................................................................................................................................12
    • Template filling stage will allocate previous extracted entities into slots in predefined template. .............................................................................................................................................13
    • Figure 2-3 Introduction of precision and recall (Maedche, 2012) ...............................................16
    • Figure 3-1 Problem solving steps (Poole and Mackworth, 2010) ...............................................25
    • Figure 3-2 Part of a light-weighted ontology with only is_a type relationship ...........................27
    • Figure 3-3 Skeletal ontology building method (Uschold et al., 1995).........................................28
    • Figure 3-4 Example of the 4-tier coding structure used in OSICS-10 (Rae and Orchard, 2007) 30
    • Figure 3-5 Upper-level hierarchy of TRAK with definitions of upper-level classes imported from the cross-referenced sources ...............................................................................................32
    • Figure 4-1 MetaMap output saved in database ............................................................................43
    • Figure 4-2 Semantic types distribution by frequency ..................................................................44
    • Figure 5-1 Ontology expansion strategies ...................................................................................51
    • Figure 5-2 Power law distribution of UMLS concepts frequency to be included into TRAK from MRI reports .........................................................................................................................52
    • Figure 5-3 Example of the term candidate normalisation process with input tear of meniscus, meniscal tear and Hoffa's fat pad .......................................................................................54
    • Figure 5-4 Part of FlexiTerm output on training set ....................................................................55
    • Figure 5-5 Power law distribution of FlexiTerm candidate termhood values .............................56
    • Figure 5-6 An example of manual annotation tags......................................................................57
    • Figure 5-8 An example of decomposition of MEDCIN item ......................................................60
    • Figure 5-9 Radlex descriptor branch screenshot..........................................................................60
    • Figure 6-1 KneeTex information extraction template represented using UML diagram ............64
    • Figure 6-2 An example of headwords and qualifiers...................................................................64
    • Figure 6-3 An example of certainty and negation qualifiers and how they are related to finding .............................................................................................................................................65
    • Figure 6-4 An example of a filled template from original text: 'There is a small undisplaced vertical radial tear of the posterior horn of the lateral meniscus.' ....................................65
    • Figure 6-13 Stagewise experiments on system generalisability by removing concepts identified from training data ................................................................................................................94
    • Abbe, A., Grouin, C., Zweigenbaum, P., Falissard, B., 2015. Text mining
    • Res. 25, 86-100. doi:10.1002/mpr.1481
    • M.A., 2011. OntoCAT--simple ontology search and integration in Java, R and
    • REST/JavaScript. BMC Bioinformatics 12, 218. doi:10.1186/1471-2105-12-218
    • Al-Safadi, L., Alomran, R., Almutairi, F., 2013. Evaluation of Metamap
    • Sciences, Engineering and Technology 22, 4231-4236.
    • Savova, G.K., 2013. Towards comprehensive syntactic and semantic annotations
    • of the clinical narrative. Proc AMIA Annu Fall Symp 20, 922-930.
    • doi:10.1136/amiajnl-2012-001317
    • Alias-i, 2008. LingPipe 4.1.0 [Online] Alias-i. Available at: http://alias-
    • i.com/lingpipe (accessed 12.13).
    • Aronson, A.R., 2006. MetaMap: Mapping Text to the UMLS Metathesaurus 1-26.
    • [17]. Bourhis, R.Y., Roth, S., MacQueen, G., 1989. Communication in the hospital setting: a survey of medical and everyday language use amongst patients, nurses and doctors. Soc Sci Med 28, 339-346.
    • [18]. Bullinaria, J.A., 2005. Semantic Networks and Frames 1-20.
    • [19]. Buntin, M.B., Burke, M.F., Hoaglin, M.C., Blumenthal, D., 2011. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood) 30, 464-471. doi:10.1377/hlthaff.2011.0178
    • [20]. Button, K., Iqbal, A.S., Letchford, R.H., van Deursen, R.W.M., 2012. Clinical effectiveness of knee rehabilitation techniques and implications for a self-care treatment model. Physiotherapy 98, 288-299. doi:10.1016/j.physio.2011.08.003
    • [21]. Button, K., Roos, P.E., van Deursen, R.W.M., 2014. Activity progression for anterior cruciate ligament injured individuals. Clin Biomech (Bristol, Avon) 29, 206-212. doi:10.1016/j.clinbiomech.2013.11.010
    • [22]. Button, K., van Deursen, R.W., Soldatova, L., Spasić, I., 2013. TRAK ontology: Defining standard care for the rehabilitation of knee conditions. Journal of Biomedical Informatics 46, 615-625. doi:10.1016/j.jbi.2013.04.009
    • [23]. Button, K.S., Ioannidis, J.P.A., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S.J., Munafò, M.R., 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365-376. doi:10.1038/nrn3475
    • [24]. Cambria, E., White, B., 2014. Jumping NLP Curves: A Review of Natural Language Processing Research. Ieee Computational Intelligence Magazine 9, 48- 57. doi:10.1109/MCI.2014.2307227
    • [25]. Campbell, D.A., Johnson, S.B., 2001. Comparing syntactic complexity in medical and non-medical corpora. Proc AMIA Symp 90-94.
    • [27]. Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., Buchanan, B.G., 2001a. A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. Journal of Biomedical Informatics 34, 301-310. doi:10.1006/jbin.2001.1029
    • [28]. Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., Buchanan, B.G., 2001b. Evaluation of negation phrases in narrative clinical reports. Proc AMIA Annu Fall Symp 105-109.
    • [29]. Chapman, W.W., Nadkarni, P.M., Hirschman, L., D'Avolio, L.W., Savova, G.K., Uzuner, Ö., 2011. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. J Am Med Inform Assoc 18, 540-543. doi:10.1136/amiajnl-2011-000465
    • [30]. Chen, Y.S., Chong, P.P., Tong, M.Y., 1994. Mathematical and computer modelling of the Pareto principle. Mathematical and Computer Modelling: An International Journal 19, 61-80. doi:10.1016/0895-7177(94)90041-8
    • [31]. Christensen, L.M., Haug, P.J., Fiszman, M., 2002. MPLUS: a probabilistic medical
    • [32]. Ciravegna, F., 1995. Understanding messages in a diagnostic domain. Information Processing & Management 31, 687-701. doi:10.1016/0306-4573(95)00027-E
    • [33]. Clark, C., Good, K., Jezierny, L., Macpherson, M., Wilson, B., Chajewska, U., 2008. Identifying smokers with a medical extraction system. Proc AMIA Annu Fall Symp 15, 36-39. doi:10.1197/jamia.M2442
    • [34]. Clauset, A., Shalizi, C.R., Newman, M.E.J., 2009. Power-Law Distributions in Empirical Data. SIAM Review 51, 661-703. doi:10.1137/070710111
    • [35]. Clayton, R.A.E., Court-Brown, C.M., 2008. The epidemiology of musculoskeletal tendinous and ligamentous injuries. Injury 39, 1338-1344. doi:10.1016/j.injury.2008.06.021
    • [36]. Cohen, W., Ravikumar, P., Fienberg, S., 2003. A Comparison of String Distance Metrics for Name-Matching Tasks.
    • [37]. Cohen, W.W., 2004. MinorThird: Methods for Identifying Names and Ontological Relations in Text using Heuristics for Inducing Regularities from Data.
    • [38]. Collins, A.M., Quillian, M.R., 1969. Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior 8, 240-247.
    • [39]. Cowie, J., Lehnert, W., 1996. Information extraction. Communications of the ACM 39, 80-91. doi:10.1145/234173.234209
    • [40]. Côté, R.G., Jones, P., Apweiler, R., Hermjakob, H., 2006. The Ontology Lookup Service, a lightweight cross-platform tool for controlled vocabulary queries. BMC Bioinformatics 7, 97. doi:10.1186/1471-2105-7-97
    • [41]. Cutting, D., Kupiec, J., Pedersen, J., Sibun, P., 1992. A practical part-of-speech tagger, in:. Presented at the the third conference, Association for Computational Linguistics, Morristown, NJ, USA, pp. 133-140. doi:10.3115/974499.974523
    • [42]. Damerau, F.J., 1964. A Technique for Computer Detection and Correction of Spelling Errors. Communications of the ACM 7, 171-176. doi:10.1145/363958.363994
    • [43]. Day-Richter, J., Harris, M.A., Haendel, M., Group, T.G.O.O.-E.W., Lewis, S., 2007. OBO-Edit-an ontology editor for biologists. Bioinformatics 23, 2198- 2200. doi:10.1093/bioinformatics/btm112
    • [44]. EMBL-EBI, 2016. Ontology Lookup Service [Online] EMBL-EBI. Available at: http://www.ebi.ac.uk/ols (accessed 16).
    • [45]. Fan, J.-W., Prasad, R., Yabut, R.M., Loomis, R.M., Zisook, D.S., Mattison, J.E., Huang, Y., 2011. Part-of-speech tagging for clinical text: wall or bridge between institutions? AMIA Annu Symp Proc 2011, 382-391. doi:10.1016/j.jbi.2011.04.006
    • [46]. Fan, J.-W., Yang, E.W., Jiang, M., Prasad, R., Loomis, R.M., Zisook, D.S., Denny, J.C., Xu, H., Huang, Y., 2013. Syntactic parsing of clinical text: guideline and corpus development with handling ill-formed sentences. J Am Med Inform Assoc 20, 1168-1177. doi:10.1136/amiajnl-2013-001810
    • [47]. Federhen, S., 2012. The NCBI Taxonomy database. Nucleic Acids Res. 40, D136- 43. doi:10.1093/nar/gkr1178
    • [48]. Fernandez-Lopez, M., Gomez-Perez, A., 2002. Overview and analysis of methodologies for building ontologies. Knowledge Engineering Review 17, 129- 156. doi:10.1017/S0269888902000462
    • [49]. Ferraro, J.P., Daumé, H., III, DuVall, S.L., Chapman, W.W., Harkema, H., Haug, P.J., 2013. Improving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation. J Am Med Inform Assoc 20, 931-939. doi:10.1136/amiajnl-2012-001453
    • [50]. Ferrucci, D., Lally, A., 2004. Building an example application with the Unstructured Information Management Architecture. IBM Systems Journal 43, 455-475. doi:10.1147/sj.433.0455
    • [51]. Fiszman, M., Blatter, D.D., Christensen, L.M., Oderich, G., Macedo, T., Eidelwein, A.P., Haug, P.J., 2002. Utilization review of head CT scans: value of a medical language processing system.
    • [52]. Fleiss, J.L., 1971. Measuring Nominal Scale Agreement Among Many Raters. Psychological Bulletin 76, 378-382.
    • [53]. Friedlin, J., Overhage, M., 2011. An evaluation of the UMLS in representing corpus derived clinical concepts. Proc AMIA Symp 2011, 435-444.
    • [54]. Friedman, C., 2006. Semantic Text Parsing for Patient Records, in: Chen, H., Fuller, S.S., Friedman, C., Hersh, W. (Eds.), Medical Informatics. Springer US, pp. 423-448. doi:10.1007/0-387-25739-X_15
    • [55]. Friedman, C., 2000. A broad-coverage natural language processing system. Proc AMIA Symp 270-274.
    • [56]. Friedman, C., 1992. The UMLS coverage of clinical radiology. Proceedings of the Annual Symposium on Computer Application in Medical Care 309.
    • [57]. Friedman, C., Alderson, P.O., Austin, J.H., Cimino, J.J., Johnson, S.B., 1994. A general natural-language text processor for clinical radiology. 1, 161-174.
    • [58]. Friedman, C., Hripcsak, G., DuMouchel, W., Johnson, S.B., Clayton, P.D., 2008. Natural language processing in an operational clinical information system. Nat. Lang. Eng. 1, 1-27. doi:10.1017/S1351324900000061
    • [59]. Friedman, C., Kra, P., Rzhetsky, A., 2002. Two biomedical sublanguages: a description based on the theories of Zellig Harris. Journal of Biomedical Informatics 35, 222-235. doi:10.1016/S1532-0464(03)00012-1
    • [60]. Funk, C., Baumgartner, W., Garcia, B., Roeder, C., Bada, M., Cohen, K.B., Hunter, L.E., Verspoor, K., 2014. Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters. BMC Bioinformatics 15, 59. doi:10.1186/1471-2105-15-59
    • [61]. Gage, B.E., McIlvain, N.M., Collins, C.L., Fields, S.K., Dawn Comstock, R., 2012. Epidemiology of 6.6 Million Knee Injuries Presenting to United States Emergency Departments From 1999 Through 2008. Academic Emergency Medicine 19, 378- 385. doi:10.1111/j.1553-2712.2012.01315.x
    • [62]. Geertzen, J., 2012. Inter-Rater Agreement with multiple raters and variables [Online] URL https://mlnl.net/jg/software/ira/ (accessed 15).
    • [63]. Gow, J., 2009. Artificial Intelligence.
    • [64]. Grenon, P., 2008. Chapter 3: A Primer on Knowledge Representation and Ontological Engineering, in: Applied Ontology, An Introduction. DE GRUYTER,
    • [65]. Grenon, P., Smith, B., Goldberg, L., 2004. Biodynamic ontology: applying BFO in the biomedical domain. Stud Health Technol Inform 102, 20-38.
    • [66]. Grishman, R., 1997. Information Extraction: Techniques and Challenges, in: Pazienza, M.T. (Ed.), Information Extraction. Springer, Heidelberg, pp. 1-18.
    • [67]. Grishman, R., Sundheim, B., 1996. Message Understanding Conference - 6: A Brief History, in:. Presented at the International Conference on Computational Linguistics, pp. 466-471.
    • [68]. Grover, M., 2012. Evaluating acutely injured patients for internal derangement of the knee. Am Fam Physician 85, 247-252.
    • [69]. Guermazi, A., Niu, J., Hayashi, D., Roemer, F.W., Englund, M., Neogi, T., Aliabadi, P., McLennan, C.E., Felson, D.T., 2012. Prevalence of abnormalities in knees detected by MRI in adults without knee osteoarthritis: population based observational study (Framingham Osteoarthritis Study). BMJ 345, e5339-e5339. doi:10.1136/bmj.e5339
    • [70]. Hammond, L.E., Lilley, J., Ribbans, W.J., 2009. Coding sports injury surveillance data: has version 10 of the Orchard Sports Injury Classification System improved the classification of sports medicine diagnoses? Br J Sports Med 43, 498-502. doi:10.1136/bjsm.2008.051979
    • [71]. Harkema, H., Dowling, J.N., Thornblade, T., Chapman, W.W., 2009. ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. Journal of Biomedical Informatics 42, 839-851. doi:10.1016/j.jbi.2009.05.002
    • [72]. Harris, Z.S., 1991. A theory of language and information. Oxford University Press, USA.
    • [73]. Hastie, T., Tibshirani, R., Friedman, J., 2008. Unsupervised Learning, in: The Elements of Statistical Learning, Springer Series in Statistics. Springer New York, New York, NY, pp. 1-101. doi:10.1007/b94608_14
    • [74]. Hayes, P.J., 1983. The Second Naive Physics Manifesto.
    • [75]. Herre, H., 2010. General Formal Ontology (GFO): A Foundational Ontology for Conceptual Modelling, in: Theory and Applications of Ontology: Computer Applications. Springer Netherlands, pp. 297-345. doi:10.1007/978-90-481-8847- 5_14
    • [76]. Hersh, W.R., Campbell, E.M., Malveau, S.E., 1997. Assessing the feasibility of large-scale natural language processing in a corpus of ordinary medical records: A lexical analysis. Proc AMIA Annu Fall Symp 580-584.
    • [77]. Hirschman, L., Sager, N., 2002. Chapter 2. Automatic Information Formatting of a Medical Sublanguage, in: Kittredge, R., Lehrberger, J. (Eds.), Sublanguage, Studies of Language in Restricted Semantic Domains. DE GRUYTER, Berlin, Boston. doi:10.1515/9783110844818-003
    • [78]. Homayouni, R., Heinrich, K., Wei, L., Berry, M.W., 2005. Gene clustering by Latent Semantic Indexing of MEDLINE abstracts. Bioinformatics 21, 104-115. doi:10.1093/bioinformatics/bth464
    • [79]. Hong, Y., Zhang, J., Heilbrun, M.E., Kahn, C.E., Jr, 2012. Analysis of RadLex Coverage and Term Co-occurrence in Radiology Reporting Templates. J Digit
    • [80]. Horridge, M., Bechhofer, S., 2011. The OWL API: A Java API for OWL ontologies. Semantic Web 2, 11-21. doi:10.3233/SW-2011-0025
    • [81]. Hripcsak, G., Austin, J.H.M., Alderson, P.O., Friedman, C., 2002. Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. Radiology 224, 157-163. doi:10.1148/radiol.2241011118
    • [82]. Hripcsak, G., Rothschild, A.S., 2005. Agreement, the f-measure, and reliability in information retrieval. 12, 296-298. doi:10.1197/jamia.M1733
    • [83]. Hsiao, C.-J., Hing, E., Socey, T.C., Cai, B., 2010. Electronic Medical Record/Electronic Health Record Systems of Office-based Physicians: United States, 2009 and Preliminary 2010 State Estimates 1-6.
    • [84]. IEEE, 1998. IEEE Standard for Developing Software Life Cycle Processes. IEEE, Piscataway, NJ, USA. doi:10.1109/IEEESTD.1998.88827
    • [85]. ihtsdo, 2014. SNOMED CT Starter Guide.
    • [86]. Ioannidis, J.P.A., 2005. Why most published research findings are false. PLoS Med. 2, e124. doi:10.1371/journal.pmed.0020124
    • [88]. Javaid, M.K., Lynch, J.A., Tolstykh, I., Guermazi, A., Roemer, F., Aliabadi, P., McCulloch, C., Curtis, J., Felson, D., Lane, N.E., Torner, J., Nevitt, M., 2010. Preradiographic MRI findings are associated with onset of knee symptoms: the most study. Osteoarthr. Cartil. 18, 323-328. doi:10.1016/j.joca.2009.11.002
    • [89]. Jensen, P.B., Jensen, L.J., Brunak, S., 2012. Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13, 395- 405. doi:10.1038/nrg3208
    • [90]. Jiang, J., 2008. Domain Adaptation in Natural Language Processing. ProQuest.
    • [91]. Jiang, M., Huang, Y., Fan, J.-W., Tang, B., Denny, J., Xu, H., 2015. Parsing clinical text: how good are the state-of-the-art parsers? MIDM) 13(S-1 15, 1. doi:10.1186/1472-6947-15-S1-S2
    • [92]. Jimeno-Yepes, A., Sticco, J.C., Mork, J.G., Aronson, A.R., 2013. GeneRIF indexing: sentence selection based on machine learning. BMC Bioinformatics 14, 171. doi:10.1186/1471-2105-14-171
    • [93]. Jonnalagadda, S., Cohen, T., Wu, S.T.-I., Gonzalez, G., 2012. Enhancing clinical concept extraction with distributional semantics. Journal of Biomedical Informatics 45, 129-140. doi:10.1016/j.jbi.2011.10.007
    • [94]. Justeson, J.S., Katz, S.M., 2008. Technical terminology: some linguistic properties and an algorithm for identification in text. Nat. Lang. Eng. 1. doi:10.1017/S1351324900000048
    • [95]. Kalibatiene, D., Vasilecas, O., 2011. Survey on Ontology Languages, in: Grabis, J., Kirikova, M. (Eds.), Perspectives in Business Informatics Research, Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 124-141. doi:10.1007/978-3-642-24511-4_10
    • [96]. Konan, S., Rayan, F., Haddad, F.S., 2009. Do physical diagnostic tests accurately detect meniscal tears? Knee Surg Sports Traumatol Arthrosc 17, 806-811.
    • [97]. Krovetz, R., 1997. Homonymy and polysemy in information retrieval, in:. Presented at the EACL '97: Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, Association for Computational Linguistics, Morristown, NJ, USA, pp. 72-79. doi:10.3115/979617.979627
    • [98]. Kwong, R.Y., Yucel, E.K., 2003. Computed tomography scan and magnetic resonance imaging, Circulation. doi:10.1161/01.CIR.0000086899.32832.EC
    • [99]. L Sumathy, K., Chidambaram, M., 2013. Text Mining: Concepts, Applications, Tools and Issues An Overview. IJCA 80, 29-32. doi:10.5120/13851-1685
    • [100]. Landis, J.R., Koch, G.G., 1977. The measurement of observer agreement for categorical data. Biometrics 33, 159-174.
    • [101]. Langlotz, C.P., 2006. RadLex: a new method for indexing online educational materials. Radiographics 26, 1595-1597. doi:10.1148/rg.266065168
    • [102]. Lehrberger, J., 2014. Sublanguage Analysis, in: Analyzing Language in Restricted Domains. Psychology Press, pp. 19-38.
    • [103]. Leroy, G., Chen, H., 2001. Meeting medical terminology needs-the ontologyenhanced Medical Concept Mapper. TITB 5, 261-270. doi:10.1109/4233.966101
    • [104]. Lewis, P., 2010. Current Trends in Information Technology.
    • [105]. Lipscomb, C.E., 2000. Medical Subject Headings (MeSH). Bulletin of the Medical Library Association 88, 265-266.
    • [106]. Lison, P., 2012. An introduction to machine learning 1-35.
    • [107]. Liu, L., Özsu, M.T. (Eds.), 2009. Encyclopedia of Database Systems. Springer US, Boston, MA. doi:10.1007/978-0-387-39940-9
    • [108]. Luyten, F.P., Denti, M., Filardo, G., Kon, E., Engebretsen, L., 2012. Definition and classification of early osteoarthritis of the knee. Knee Surg Sports Traumatol Arthrosc 20, 401-406. doi:10.1007/s00167-011-1743-2
    • [109]. Maedche, A., 2012. Ontology Learning for the Semantic Web. Springer Science & Business Media, Boston, MA. doi:10.1007/978-1-4615-0925-7
    • [110]. Maimon, O., Rokach, L., 2005. Introduction to Supervised Methods, in: Maimon, O., Rokach, L. (Eds.), Data Mining and Knowledge Discovery Handbook. Springer US, New York, pp. 149-164. doi:10.1007/0-387-25465-X_8
    • [111]. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D., 2014. The Stanford CoreNLP Natural Language Processing Toolkit, in:. Presented at the 52nd Annual Meeting of the Association for Computational Linguistics System Demonstrations, pp. 55-60.
    • [112]. Mansouri, A., Affendey, L.S., Mamat, A., 2008. Named entity recognition approaches. International Journal of Computer Science and Network Security.
    • [113]. Manzoor, U., Usman, M., A, M., Mueen, A., 2015. Ontology-Based Clinical Decision Support System for Predicting High-Risk Pregnant Woman. International Journal of Advanced Computer Science and Applications 6. doi:10.14569/IJACSA.2015.061228
    • [114]. Mate, S., Koepcke, F., Toddenroth, D., Martin, M., Prokosch, H.-U., Buerkle, T., Ganslandt, T., 2015. Ontology-Based Data Integration between Clinical and
    • [115]. Maxwell, J.L., Keysor, J.J., Niu, J., Singh, J.A., Wise, B.L., Frey-Law, L., Nevitt, M.C., Felson, D.T., 2013. Participation following knee replacement: the MOST cohort study. Phys Ther 93, 1467-1474. doi:10.2522/ptj.20130109
    • [116]. Maynard, D., Peters, W., Li, Y., 2006. Metrics for Evaluation of Ontology-based Information Extraction, in:. Presented at the WWW Workshop on Evaluation of Ontologies for the Web, pp. 1-8.
    • [117]. McCarthy, J., 1987. Generality in artificial intelligence. Communications of the ACM 30, 1030-1035. doi:10.1145/33447.33448
    • [119]. Merriam-Webster, n.d. Merriam-Webster Medical Dictionary [Online] MerriamWebster. URL www.merriam-webster.com (accessed 12).
    • [120]. Meystre, S.M., Savova, G.K., Kipper-Schuler, K.C., Hurdle, J.F., 2008. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 128-144.
    • [121]. Moens, M.-F., 2006. Information Extraction: Algorithms and Prospects in a Retrieval Context. Springer Science & Business Media. doi:10.1007/978-1-4020- 4993-4
    • [122]. Mohanty, S.K., Piccoli, A.L., Devine, L.J., Patel, A.A., William, G.C., Winters, S.B., Becich, M.J., Parwani, A.V., 2007. Synoptic tool for reporting of hematological and lymphoid neoplasms based on World Health Organization classification and College of American Pathologists checklist. BMC Cancer 7, 144. doi:10.1186/1471-2407-7-144
    • [133]. OpenNLP, 2010. Apache OpenNLP [Online] The Apache Software Foundation. URL https://opennlp.apache.org (accessed 3.12).
    • [135]. Pessis, E., Drapé, J.-L., Ravaud, P., Chevrot, A., Dougados, M., Ayral, X., 2003. Assessment of progression in knee osteoarthritis: results of a 1 year study comparing arthroscopy and MRI. Osteoarthr. Cartil. 11, 361-369.
    • [137]. Piskorski, J., Yangarber, R., 2013. Information Extraction: Past, Present and Future, in: Multi-Source, Multilingual Information Extraction and Summarization. pp. 23-49. doi:10.1007/978-3-642-28569-1__2
    • [139]. Poole, D.L., Mackworth, A.K., 2010. Artificial Intelligence. Cambridge University Press, Cambridge. doi:10.1017/CBO9780511794797
    • [152]. Sager, N., Lyman, M., Bucknall, C., Nhan, N., Tick, L.J., 1994. Natural language processing and the representation of clinical data. 1, 142-160.
    • [153]. Salton, G., Buckley, C., 1988. Term-weighting approaches in automatic text retrieval. Information Processing & Management 24, 513-523. doi:10.1016/0306- 4573(88)90021-0
    • [154]. Savova, G.K., Masanz, J.J., Ogren, P.V., Zheng, J., Sohn, S., Kipper-Schuler, K.C., Chute, C.G., 2010. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. 17, 507-513. doi:10.1136/jamia.2009.001560
    • [155]. Schattner, P., Saunders, M., Stanger, L., Speak, M., Russo, K., 2010. Clinical data extraction and feedback in general practice: a case study from Australian primary care. jhi 18, 205-212. doi:10.14236/jhi.v18i3.773
    • [156]. Scheuermann, R.H., Ceusters, W., Smith, B., 2009. Toward an ontological treatment of disease and diagnosis. Summit on Translat Bioinforma 2009, 116- 120.
    • [157]. Schriml, L.M., Arze, C., Nadendla, S., Chang, Y.-W.W., Mazaitis, M., Felix, V., Feng, G., Kibbe, W.A., 2012. Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res. 40, 940-946. doi:10.1093/nar/gkr972
    • [158]. Sheeba, J.I., Vivekanandan, K., Sabitha, G., Padmavathi, P., 2013. Unsupervised Hidden Topic Framework for Extracting Keywords (Synonym, Homonym, Hyponymy and Polysemy) and Topics in Meeting Transcripts, in: Advances in Computing and Information Technology. Springer Berlin Heidelberg, pp. 299- 307. doi:10.1007/978-3-642-31552-7_32
    • [159]. Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg, L.J., Eilbeck, K., Ireland, A., Mungall, C.J., Leontis, N., Rocca-Serra, P., Ruttenberg, A., Sansone, S.-A., Scheuermann, R.H., Shah, N., Whetzel, P.L., Lewis, S., 2007. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25, 1251-1255. doi:10.1038/nbt1346
    • [160]. Sonin, A.H., Fitzgerald, S.W., Bresler, M.E., Kirsch, M.D., Hoff, F.L., Friedman, H., 1995. MR imaging appearance of the extensor mechanism of the knee: functional anatomy and injury patterns. Radiographics 15, 367-382. doi:10.1148/radiographics.15.2.7761641
    • [161]. Sorel, D., 2015. jQuery QueryBuilder.
    • [162]. Spasic, I., Ananiadou, S., McNaught, J., Kumar, A., 2005. Text mining and ontologies in biomedicine: Making sense of raw text. Brief. Bioinformatics 6, 239-
    • [163]. Spasić, I., Greenwood, M., Preece, A., Francis, N., Elwyn, G., 2013. FlexiTerm: a flexible term recognition method. J Biomed Semantics 4, 27. doi:10.1186/2041- 1480-4-27
    • [179]. U.S. National Library of Medicine, 2004. Fact SheetMEDLINE®.
    • [180]. UK Department of Health, 2003. Confidentiality.
    • [182]. UMLS, n.d. UMLS Fact Sheet [Online] U.S. National Library of Medicine. Available at: http://www.nlm.nih.gov/pubs/factsheets/umlslex.html (accessed 15).
    • [185]. Uschold, M., King, M., Moralee, S., Zorgios, Y., 1998. The Enterprise Ontology. Knowledge Engineering Review 13, 31-89. doi:10.1017/S0269888998001088
    • [210]. Yetisgen-Yildiz, M., Gunn, M.L., Xia, F., Payne, T.H., 2011. Automatic identification of critical follow-up recommendation sentences in radiology reports. Proc AMIA Symp 2011, 1593-1602.
    • [211]. Zeng, Q.T., Redd, D., Divita, G., Jarad, S., Brandt, C., Nebeker, J.R., 2011. Characterizing Clinical Text and Sublanguage: A Case Study of the VA Clinical Notes. J Health Med Inform. doi:10.4172/2157-7420.S3-001
  • Inferred research data

    The results below are discovered through our pilot algorithms. Let us know how we are doing!

    Title Trust
    73
    73%
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article