Cross Disciplinary Consultancy to Bridge Public Health Technical Needs and Analytic Developers: Negation Detection Use Case

This paper describes a continuing initiative of the International Society for Disease Surveillance designed to bring together public health practitioners and analytics solution developers from both academia and industry. Funded by the Defense Threat Reduction Agency, a series of consultancies have been conducted on a range of topics of pressing concern to public health (e.g. developing methods to enhance prediction of asthma exacerbation, developing tools for asyndromic surveillance from chief complaints). The topic of this final consultancy, conducted at the University of Utah in January 2017, is focused on defining a roadmap for the development of algorithms, tools, and datasets for improving the capabilities of text processing algorithms to identify negated terms (i.e. negation detection) in free-text chief complaints and triage reports.

[1]  Snehal N Shah,et al.  Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston , 2016, Online journal of public health informatics.

[2]  Martijn J. Schuemie,et al.  ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus , 2014, BMC Bioinformatics.

[3]  David Tresner-Kirsch,et al.  MITRE system for clinical assertion status classification , 2011, J. Am. Medical Informatics Assoc..

[4]  Debbie A. Travers,et al.  Defining and Applying a Method for Improving the Sensitivity and Specificity of an Emergency Department Early Event Detection System , 2007, AMIA.

[5]  Hong Yu,et al.  Biomedical negation scope detection with conditional random fields , 2010, J. Am. Medical Informatics Assoc..

[6]  R. Field,et al.  The Impact of Law on Syndromic Disease Surveillance Implementation , 2018, Journal of public health management and practice : JPHMP.

[7]  Mike Conway,et al.  Using chief complaints for syndromic surveillance: A review of chief complaint based classifiers in North America , 2013, J. Biomed. Informatics.

[8]  Hongfang Liu,et al.  DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx , 2015, J. Biomed. Informatics.

[9]  Maria Skeppstedt,et al.  Negation detection in Swedish clinical text: An adaption of NegEx to Swedish , 2011, J. Biomed. Semant..

[10]  Shuying Shen,et al.  2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text , 2011, J. Am. Medical Informatics Assoc..

[11]  Mike Conway,et al.  Cross-Disciplinary Consultancy to Bridge Public Health Technical Needs and Analytic Developers: Asyndromic Surveillance Use Case , 2015, Online Journal of Public Health Informatics.

[12]  Jon Patrick,et al.  Automatic negation detection in narrative pathology reports , 2015, Artif. Intell. Medicine.

[13]  Jihye Choi,et al.  Web-based infectious disease surveillance systems and public health perspectives: a systematic review , 2016, BMC Public Health.

[14]  Yang Huang,et al.  A novel hybrid approach to automated negation detection in clinical radiology reports. , 2007, Journal of the American Medical Informatics Association : JAMIA.

[15]  Özlem Uzuner,et al.  Machine learning and rule-based approaches to assertion classification. , 2009, Journal of the American Medical Informatics Association : JAMIA.

[16]  James J. Masanz,et al.  Negation’s Not Solved: Generalizability Versus Optimizability in Clinical Natural Language Processing , 2014, PloS one.

[17]  Prakash M. Nadkarni,et al.  Research Paper: Use of General-purpose Negation Detection to Augment Concept Indexing of Medical Documents: A Quantitative Study Using the UMLS , 2001, J. Am. Medical Informatics Assoc..

[18]  L. Hutwagner,et al.  Multifaceted syndromic surveillance in a public health department using the early aberration reporting system. , 2005, Journal of public health management and practice : JPHMP.

[19]  Maria Kvist,et al.  Negation Scope Delimitation in Clinical Text Using Three Approaches: NegEx, PyConTextNLP and SynNeg , 2013, NODALIDA.

[20]  Anita Burgun-Parenthoine,et al.  Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse , 2017, J. Am. Medical Informatics Assoc..

[21]  Bonnie L. Webber,et al.  Neural Networks For Negation Scope Detection , 2016, ACL.

[22]  Dezon Finch,et al.  TagLine: Information Extraction for Semi-Structured Text Elements In Medical Progress Notes , 2012 .

[23]  Stephanie W. Haas,et al.  Evaluation of emergency medical text processor, a system for cleaning chief complaint text data. , 2004, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[24]  Alice Min,et al.  Syndromic Surveillance for Influenza in the Emergency Department–A Systematic Review , 2013, PloS one.

[25]  Eric Fosler-Lussier,et al.  Extending NegEx with Kernel Methods for Negation Detection in Clinical Text , 2015 .

[26]  David Kauchak,et al.  NegAIT: A new parser for medical text simplification using morphological, sentential and double negation , 2017, J. Biomed. Informatics.

[27]  Cyril Grouin,et al.  Detecting negation of medical problems in French clinical notes , 2012, IHI '12.

[28]  Sunghwan Sohn,et al.  Dependency Parser-based Negation Detection in Clinical Narratives , 2012, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[29]  Howard S Burkom.  Evolution of Public Health Surveillance: Status and Recommendations. , 2017, American journal of public health.

[30]  Dezon Finch,et al.  TagLine: Information Extraction for Semi-Structured Text in Medical Progress Notes , 2014, AMIA.

[31]  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.

[32]  Wendy W. Chapman,et al.  A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries , 2001, J. Biomed. Informatics.

[33]  Wendy W. Chapman,et al.  ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports , 2009, J. Biomed. Informatics.

[34]  Ernestina Menasalvas Ruiz,et al.  An Approach to Detect Negation on Medical Documents in Spanish , 2014, Brain Informatics and Health.