Shades of Certainty: Annotation and Classification of Swedish Medical Records
暂无分享,去创建一个
[1] Sumithra Velupillai,et al. Automatic Classification of Factuality Levels : A Case Study on Swedish Diagnoses and the Impact of Local Context , 2011, LBM 2011.
[2] A Hasman,et al. Medical narratives in electronic medical records. , 1997, International journal of medical informatics.
[3] Sumithra Velupillai,et al. How Certain are Clinical Assessments? Annotating Swedish Clinical Text for (Un)certainties, Speculations and Negations , 2010, LREC.
[4] Nigel Collier,et al. The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers , 1999, EACL.
[5] George Lakoff,et al. Hedges: A study in meaning criteria and the logic of fuzzy concepts , 1973, J. Philos. Log..
[6] Özlem Uzuner,et al. Extracting medication information from clinical text , 2010, J. Am. Medical Informatics Assoc..
[7] Pamela Jordan. Basics of qualitative research: Grounded theory procedures and techniques , 1994 .
[8] J. R. Landis,et al. The measurement of observer agreement for categorical data. , 1977, Biometrics.
[9] Wendy W. Chapman,et al. A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries , 2001, J. Biomed. Informatics.
[10] Mary McGee Wood,et al. Squibs and Discussions: Evaluating Discourse and Dialogue Coding Schemes , 2005, CL.
[11] Stephan Oepen,et al. Resolving Speculation: MaxEnt Cue Classification and Dependency-Based Scope Rules , 2010, CoNLL Shared Task.
[12] Noriko Kando,et al. Certainty Identification in Texts: Categorization Model and Manual Tagging Results , 2023 .
[13] János Csirik,et al. The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes , 2008, BMC Bioinformatics.
[14] D. Timmermans,et al. The Roles of Experience and Domain of Expertise in Using Numerical and Verbal Probability Terms in Medical Decisions , 1994, Medical decision making : an international journal of the Society for Medical Decision Making.
[15] Galia Angelova,et al. Extraction and Exploration of Correlations in Patient Status Data , 2009, BiomedicalIE@RANLP.
[16] Antoine Geissbühler,et al. Using lexical disambiguation and named-entity recognition to improve spelling correction in the electronic patient record , 2003, Artif. Intell. Medicine.
[17] Roser Morante,et al. Learning the Scope of Hedge Cues in Biomedical Texts , 2009, BioNLP@HLT-NAACL.
[18] Halil Kilicoglu,et al. Recognizing speculative language in biomedical research articles: a linguistically motivated perspective , 2008, BMC Bioinformatics.
[19] Roser Morante,et al. Memory-Based Resolution of In-Sentence Scopes of Hedge Cues , 2010, CoNLL Shared Task.
[20] Ramin Khorasani,et al. Is terminology used effectively to convey diagnostic certainty in radiology reports? , 2003, Academic radiology.
[21] Ola Knutsson,et al. A Robust Shallow Parser for Swedish , 2003 .
[22] Wendy W. Chapman,et al. ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports , 2009, J. Biomed. Informatics.
[23] Peter Szolovits,et al. Evaluating the state-of-the-art in automatic de-identification. , 2007, Journal of the American Medical Informatics Association : JAMIA.
[24] Christian Lovis,et al. Power of expression in the electronic patient record: structured data or narrative text? , 2000, Int. J. Medical Informatics.
[25] Pierre Zweigenbaum,et al. Semi-Automated Extension of a Specialized Medical Lexicon for French , 2010, LREC.
[26] Ricardo Baeza-Yates,et al. Modern Information Retrieval - the concepts and technology behind search, Second edition , 2011 .
[27] James Pustejovsky,et al. A factuality profiler for eventualities in text , 2008 .
[28] David A. Campbell,et al. Comparing syntactic complexity in medical and non-medical corpora , 2001, AMIA.
[29] Dimitrios Kokkinakis,et al. Identification of Entity References in Hospital Discharge Letters , 2007, NODALIDA.
[30] Chenjie Xia,et al. How Doctors Think , 2008, McGill Journal of Medicine : MJM.
[31] Hercules Dalianis,et al. Creating and evaluating a consensus for negated and speculative words in a Swedish clinical corpus , 2010, NeSp-NLP@ACL.
[32] John Mingers,et al. Combining IS Research Methods: Towards a Pluralist Methodology , 2001, Inf. Syst. Res..
[33] Ron Artstein,et al. Survey Article: Inter-Coder Agreement for Computational Linguistics , 2008, CL.
[34] Sumithra Velupillai,et al. Diagnosing Diagnoses in Swedish Clinical Records , 2008 .
[35] Ted Briscoe,et al. Weakly Supervised Learning for Hedge Classification in Scientific Literature , 2007, ACL.
[36] S. Meystre,et al. Automatic de-identification of textual documents in the electronic health record: a review of recent research , 2010, BMC medical research methodology.
[37] János Csirik,et al. The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text , 2010, CoNLL Shared Task.
[38] Xiaolong Wang,et al. A Cascade Method for Detecting Hedges and their Scope in Natural Language Text , 2010, CoNLL Shared Task.
[39] Hanna Suominen. Machine Learning and Clinical Text. Supporting Health Information Flow , 2009 .
[40] Maria Kvist,et al. Fine-Grained Certainty Level Annotations Used for Coarser-Grained E-Health Scenarios - Certainty Classification of Diagnostic Statements in Swedish Clinical Text , 2012, CICLing.
[41] Inderjeet Mani,et al. Protein Name Tagging Guidelines: Lessons Learned , 2005, Comparative and functional genomics.
[42] H. Tange. How to approach the structuring of the medical record? Towards a model for flexible access to free text medical data. , 1996, International Journal of Bio-Medical Computing.
[43] Daniel Gildea,et al. The Proposition Bank: An Annotated Corpus of Semantic Roles , 2005, CL.
[44] Peter Szolovits,et al. A de-identifier for medical discharge summaries , 2008, Artif. Intell. Medicine.
[45] H. Dalianis,et al. The Stockholm EPR Corpus – Characteristics and Some Initial Findings , 2009 .
[46] A K Dixon,et al. Communication of doubt and certainty in radiological reports. , 2000, The British journal of radiology.
[47] Beth Sundheim,et al. MUC-5 Evaluation Metrics , 1993, MUC.
[48] Róbert Busa-Fekete,et al. State-of-the-art anonymization of medical records using an iterative machine learning framework. , 2007 .
[49] Heljä Lundgrén-Laine,et al. Characteristics of Finnish and Swedish intensive care nursing narratives: a comparative analysis to support the development of clinical language technologies , 2011, J. Biomed. Semant..
[50] Hercules Dalianis,et al. SweNam-A Swedish Named Entity recognizer Its construction, training and evaluation , 2001 .
[51] Pierre Zweigenbaum,et al. Testing Tactics to Localize De-Identification , 2009, MIE.
[52] Özlem Uzuner,et al. Viewpoint Paper: Recognizing Obesity and Comorbidities in Sparse Data , 2009, J. Am. Medical Informatics Assoc..
[53] Mary M Christopher,et al. Cytologic diagnosis: expression of probability by clinical pathologists. , 2004, Veterinary clinical pathology.
[54] Wendy W. Chapman,et al. Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm , 2011, J. Biomed. Informatics.
[55] Dragomir R. Radev,et al. Detecting Speculations and their Scopes in Scientific Text , 2009, EMNLP.
[56] Peter Szolovits,et al. Automated de-identification of free-text medical records , 2008, BMC Medical Informatics Decis. Mak..
[57] Alfonso Valencia,et al. Overview of BioCreAtIvE: critical assessment of information extraction for biology , 2005, BMC Bioinformatics.
[58] Maria Kvist,et al. Factuality Levels of Diagnoses in Swedish Clinical Text , 2011, MIE.
[59] Mark Liberman,et al. A formal framework for linguistic annotation , 1999, Speech Commun..
[60] L. Sweeney. Replacing personally-identifying information in medical records, the Scrub system. , 1996, Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium.
[61] Sunghwan Sohn,et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications , 2010, J. Am. Medical Informatics Assoc..
[62] Janyce Wiebe,et al. A Corpus Study of Evaluative and Speculative Language , 2001, SIGDIAL Workshop.
[63] Claire Cardie,et al. Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.
[64] Philip V. Ogren,et al. Knowtator: A Protégé plug-in for annotated corpus construction , 2006, NAACL.
[65] Randolph A. Miller,et al. Identifying QT prolongation from ECG impressions using a general-purpose Natural Language Processor , 2009, Int. J. Medical Informatics.
[66] Magnus Sahlgren,et al. The Word-Space Model: using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces , 2006 .
[67] Hagit Shatkay,et al. New directions in biomedical text annotation: definitions, guidelines and corpus construction , 2006, BMC Bioinformatics.
[68] Sumithra Velupillai,et al. Developing a standard for de-identifying electronic patient records written in Swedish: Precision, recall and F-measure in a manual and computerized annotation trial , 2009, Int. J. Medical Informatics.
[69] D. Clark. Verbal uncertainty expressions: A critical review of two decades of research , 1990 .
[70] Özlem Uzuner,et al. Role of Local Context in Automatic Deidentification of Ungrammatical, Fragmented Text , 2006, NAACL.
[71] Wendy W. Chapman,et al. Evaluation of negation phrases in narrative clinical reports , 2001, AMIA.
[72] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[73] Robert Wayne Gregory,et al. Design Science Research and the Grounded Theory Method: Characteristics, Differences, and Complementary Uses , 2010, ECIS.
[74] John F. Hurdle,et al. Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research , 2008, Yearbook of Medical Informatics.
[75] Petra Saskia Bayerl,et al. What Determines Inter-Coder Agreement in Manual Annotations? A Meta-Analytic Investigation , 2011, CL.
[76] J. Nuyts. Epistemic Modality, Language, and Conceptualization: A cognitive-pragmatic perspective , 2001 .
[77] György Szarvas,et al. Hedge Classification in Biomedical Texts with a Weakly Supervised Selection of Keywords , 2008, ACL.
[78] Stéfan Jacques Darmoni,et al. Natural Language Processing to Detect Risk Patterns Related to Hospital Acquired Infections , 2009, BiomedicalIE@RANLP.
[79] Beatrice Santorini,et al. Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.
[80] J. Tritter,et al. Medical error: a discussion of the medical construction of error and suggestions for reforms of medical education to decrease error , 2001, Medical education.
[81] Wendy W. Chapman,et al. Inductive creation of an annotation schema for manually indexing clinical conditions from emergency department reports , 2006, J. Biomed. Informatics.
[82] James Pustejovsky,et al. FactBank: a corpus annotated with event factuality , 2009, Lang. Resour. Evaluation.
[83] Padmini Srinivasan,et al. The Language of Bioscience: Facts, Speculations, and Statements In Between , 2004, HLT-NAACL 2004.
[84] Fredrik Olsson,et al. Bootstrapping Named Entity Annotation by Means of Active Machine Learning: A Method for Creating Corpora , 2008 .
[85] Özlem Uzuner,et al. Machine learning and rule-based approaches to assertion classification. , 2009, Journal of the American Medical Informatics Association : JAMIA.
[86] Dennis Reidsma,et al. Exploiting ‘Subjective’ Annotations , 2008, COLING 2008.
[87] Sumithra Velupillai,et al. De-identifying Swedish clinical text - refinement of a gold standard and experiments with Conditional random fields , 2010, J. Biomed. Semant..
[88] Joel D. Martin,et al. Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010 , 2011, J. Am. Medical Informatics Assoc..
[89] Silja Renooij,et al. Talking probabilities: communicating probabilistic information with words and numbers , 1999, Int. J. Approx. Reason..
[90] Daniel L. Rubin,et al. Evaluation of Negation and Uncertainty Detection and its Impact on Precision and Recall in Search , 2009, Journal of Digital Imaging.
[91] Shuying Shen,et al. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text , 2011, J. Am. Medical Informatics Assoc..
[92] Tapio Salakoski,et al. Parsing Clinical Finnish: Experiments with Rule-Based and Statistical Dependency Parsers , 2009, NODALIDA.
[93] Catherine F. Schryer,et al. A certain art of uncertainty: case presentation and the development of professional identity. , 2003, Social science & medicine.