title: Restricted natural language based querying of clinical databases.
abstract: OBJECTIVE
  To elevate the level of care to the community it is essential to provide usable tools for healthcare professionals to extract knowledge from clinical data. In this paper a generic translation algorithm is proposed to translate a restricted natural language query (RNLQ) to a standard query language like SQL (Structured Query Language).
  
  
  METHODS
  A special purpose clinical data analytics language (CliniDAL) has been introduced which provides scheme of six classes of clinical questioning templates. A translation algorithm is proposed to translate the RNLQ of users to SQL queries based on a similarity-based Top-k algorithm which is used in the mapping process of CliniDAL. Also a two layer rule-based method is used to interpret the temporal expressions of the query, based on the proposed temporal model. The mapping and translation algorithms are generic and thus able to work with clinical databases in three data design models, including Entity-Relationship (ER), Entity-Attribute-Value (EAV) and XML, however it is only implemented for ER and EAV design models in the current work.
  
  
  RESULTS
  It is easy to compose a RNLQ via CliniDAL's interface in which query terms are automatically mapped to the underlying data models of a Clinical Information System (CIS) with an accuracy of more than 84% and the temporal expressions of the query comprising absolute times, relative times or relative events can be automatically mapped to time entities of the underlying CIS and to normalized temporal comparative values.
  
  
  CONCLUSIONS
  The proposed solution of CliniDAL using the generic mapping and translation algorithms which is enhanced by a temporal analyzer component provides a simple mechanism for composing RNLQ for extracting knowledge from CISs with different data design models for analytics purposes.
authors: Health Language Laboratories, School of Information Technologies, The University of Sydney, NSW, Australia; School of Computer Engineering, The University of Zanjan, Zanjan, Iran. Electronic address: lsaf7301@uni.sydney.edu.au.
authors: Leila
authors: L
authors: Safari
authors: Health Language Laboratories, School of Information Technologies, The University of Sydney, NSW, Australia; Innovative Clinical Information Management Systems, Sydney, Australia. Electronic address: jonpat@icims.com.au.
authors: Jon D
authors: JD
authors: Patrick
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D009323:Natural Language Processing; D012984:Software; D018875:Vocabulary, Controlled
keywords: Data analytics; Knowledge discovery and reuse; Restricted natural language querying