IMPLEMENTING EFORM-BASED BASELINE RISK DATA EXTRACTION FROM HIGH QUALITY PAPERS FOR THE BRISKET DATABASE AND TOOL

Abstract

This thesis was undertaken to investigate if an eForm-based extractor interface would improve the efficiency of the baseline risk extraction process for BRiskeT (Baseline Risk e-Tool). The BRiskeT database will contain the extracted baseline risk data from top prognostic research articles. BRiskeT utilizes McMaster University’s PLUS (Premium Literature Service) database to thoroughly vet articles prior to their inclusion in BRiskeT. The articles that have met inclusion criteria are then passed into the extractor interface that was developed for the purpose of this thesis, which has been called MacPrognosis. MacPrognosis displays these articles to a data extractor who fills out an electronic form which gives an overview of the baseline risk information in an article. The baseline risk information is subsequently saved to the BRiskeT database, which can then be queried according to the end user’s needs. One of the goals in switching from a paper-based extraction system to an eForm-based system was to save time in the extraction process. Another goal for MacPrognosis was to create an eForm that allowed baseline risk information to be extracted from as many disciplines as possible. To test whether MacPrognosis succeeded in saving extraction time and improving the proportion of articles from which baseline risk data could be extracted, it was subsequently utilized to extract data from a large test set of articles. The results of the extraction process were then compared with results from a previously conducted data extraction pilot utilizing a paper-based system which was created during the feasibility analysis for BRiskeT in 2012. The new eForm based extractor interface not only sped up the process of data extraction, but may also increase the proportion of articles from which data can be successfully extracted with minor future alterations when compared to a paper-based model of extraction.