A Search Engine for Structured Health Data
Abstract
This paper presents the architecture of a health data search engine, along with preliminary findings that demonstrate the feasibility of the approach taken. The work is motivated by the need to incorporate information about similar patients into clinical decision making, and by the need to develop a tool that can search for similar patients in health data repositories. Central to the design of the search engine is the use of clustering analysis within health data repositories to ensure that responses to queries consist of data summaries that do not violate the confidentiality of patient records. Recent results concerning the feasibility of this search engine approach are reviewed. These results speak to the relative ease of creating clinically meaningful summaries of patient types, and to the accuracy of predictions made using the summarized data. The paper concludes with a brief discussion of further work required to implement a health data search engine and to demonstrate its effectiveness.
Authors: Mark Chignell, Mahsa Rouzbahman
Published in: TSpace (University of Toronto) (2014)