A temporal model for Clinical Data Analytics language

The proposal of a special purpose language for Clinical Data Analytics (CliniDAL) is presented along with a general model for expressing temporal events in the language. The temporal dimension of clinical data needs to be addressed from at least five different points of view. Firstly, how to attach the knowledge of time based constraints to queries; secondly, how to mine temporal data in different CISs with various data models; thirdly, how to deal with both relative time and absolute time in the query language; fourthly, how to tackle internal time-event dependencies in queries, and finally, how to manage historical time events preserved in the patient's narrative. The temporal elements of the language are defined in Bachus Naur Form (BNF) along with a UML schema. Its use in a designed taxonomy of a five class hierarchy of data analytics tasks shows the solution to problems of time event dependencies in a highly complex cascade of queries needed to evaluate scientific experiments. The issues in using the model in a practical way are discussed as well.

[1]  Abraham Bernstein,et al.  Applied Temporal RDF: Efficient Temporal Querying of RDF Data with SPARQL , 2009, ESWC.

[2]  George Hripcsak,et al.  A temporal constraint structure for extracting temporal information from clinical narrative , 2006, J. Biomed. Informatics.

[3]  Koichi Takeda,et al.  MedTAKMI-CDI: Interactive knowledge discovery for clinical decision intelligence , 2007, IBM Syst. J..

[4]  T. Wang,et al.  Interactive Information Visualization for Exploring and Querying Electronic Health Records : A Systematic Review , 2011 .

[5]  Yuval Shahar,et al.  Intelligent visualization and exploration of time-oriented data of multiple patients , 2010, Artif. Intell. Medicine.

[6]  Jon Patrick,et al.  KNOWLEDGE DISCOVERY AND KNOWLEDGE REUSE IN CLINICAL INFORMATION SYSTEMS , 2013, BioMed 2013.

[7]  Ben Shneiderman,et al.  Visual information seeking in multiple electronic health records: design recommendations and a process model , 2010, IHI.

[8]  Martin J. O'Connor,et al.  SQWRL: A Query Language for OWL , 2009, OWLED.

[9]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[10]  Angelo Montanari,et al.  The t4sql temporal query language , 2007, CIKM '07.

[11]  George Hripcsak,et al.  Temporal reasoning with medical data - A review with emphasis on medical natural language processing , 2007, J. Biomed. Informatics.

[12]  Christopher G Chute,et al.  CNTRO: A Semantic Web Ontology for Temporal Relation Inferencing in Clinical Narratives. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[13]  George Hripcsak,et al.  System Architecture for Temporal Information Extraction, Representationand Reasoning in Clinical Narrative Reports , 2005, AMIA.

[14]  Richard T. Snodgrass,et al.  The TSQL2 Temporal Query Language , 1995 .

[15]  Ben Shneiderman,et al.  Finding comparable temporal categorical records: A similarity measure with an interactive visualization , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.