Rewriting Natural Language Queries Using Patterns

In this paper, a method based on pre-defined patterns, which rewrites natural language queries into a multi-layer, flexible, scalable and object-oriented query language, is presented. The method has been conceived to assist physicians in their search for clinical information in an Electronic Health Records system. Indeed, the query language of the system being difficult to handle for physicians, this method allows querying using natural language vs. using dedicated object-oriented query language. The information extraction method that has been developed can be seen as a named entity recognition system based on regular expressions that tags pieces of the query. The patterns are constructed recursively from the initial natural language query and from atomic patterns that correspond to the entities, the relationships and the constraints of the underlying model representing Electronic Health Records. Further evaluation is needed, but the preliminary results obtained by testing a set of natural language queries are very encouraging.

[1]  William R. Hersh,et al.  Barriers to Retrieving Patient Information from Electronic Health Record Data: Failure Analysis from the TREC Medical Records Track , 2012, AMIA.

[2]  Jaco Voorham,et al.  Case Report: Computerized Extraction of Information on the Quality of Diabetes Care from Free Text in Electronic Patient Records of General Practitioners , 2007, J. Am. Medical Informatics Assoc..

[3]  Dezon Finch,et al.  Using Information from the Electronic Health Record to Improve Measurement of Unemployment in Service Members and Veterans with mTBI and Post-Deployment Stress , 2014, PloS one.

[4]  Frédérique Segond,et al.  Separate the grain from the chaff: make the best use of language and knowledge technologies to model textual medical data extracted from electronic health records , 2013 .

[5]  Julien Grosjean,et al.  Health multi-terminology portal: a semantic added-value for patient safety. , 2011, Studies in health technology and informatics.

[6]  Abraham Bernstein,et al.  Evaluating the usability of natural language query languages and interfaces to Semantic Web knowledge bases , 2010, J. Web Semant..

[7]  Serguei V. S. Pakhomov,et al.  High Throughput Modularized NLP System for Clinical Text , 2005, ACL.

[8]  Ollivier Haemmerlé,et al.  Swip: A Natural Language to SPARQL Interface Implemented with SPARQL , 2014, ICCS.

[9]  Duy Duc An Bui,et al.  Research and applications: Learning regular expressions for clinical text classification , 2014, J. Am. Medical Informatics Assoc..

[10]  Lina Fatima Soualmia,et al.  Indexing biomedical documents with a possibilistic network , 2016, J. Assoc. Inf. Sci. Technol..

[11]  M. Schuemie,et al.  Automating classification of free‐text electronic health records for epidemiological studies , 2012, Pharmacoepidemiology and drug safety.

[12]  Richard W. Grant,et al.  Case Report: Using Regular Expressions to Abstract Blood Pressure and Treatment Intensification Information from the Text of Physician Notes , 2006, J. Am. Medical Informatics Assoc..

[13]  Shlomo Geva,et al.  XML Retrieval with a Natural Language Interface , 2005, SPIRE.

[14]  Stéfan Jacques Darmoni,et al.  Moteur de recherche sémantique au sein du dossier du patient informatisé : langage de requêtes spécifique , 2014, JFIM.

[15]  Ricky K. Taira,et al.  A statistical natural language processor for medical reports , 1999, AMIA.

[16]  Son Doan,et al.  Application of information technology: MedEx: a medication information extraction system for clinical narratives , 2010, J. Am. Medical Informatics Assoc..

[17]  Scott T. Weiss,et al.  Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system , 2006, BMC Medical Informatics Decis. Mak..

[18]  Seán O'Riain,et al.  Answering natural language queries over linked data graphs: a distributional semantics approach , 2013, SIGIR.

[19]  Ollivier Haemmerlé,et al.  Natural language query interpretation into SPARQL using patterns , 2013 .

[20]  James F. Allen.  Natural language understanding , 1987, Bejnamin/Cummings series in computer science.

[21]  C. DesRoches,et al.  A progress report on electronic health records in U.S. hospitals. , 2010, Health affairs.

[22]  Stéfan Jacques Darmoni,et al.  RAVEL: Retrieval And Visualization in ELectronic health records , 2012, MIE.

[23]  T. Lavergne,et al.  Combining an Expert-Based Medical Entity Recognizer to a Machine-Learning System: Methods and a Case Study , 2013, Biomedical informatics insights.

[24]  A Hasman,et al.  Medical narratives in electronic medical records. , 1997, International journal of medical informatics.