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. 2017 May 1;24(3):607-613.
doi: 10.1093/jamia/ocw144.

Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse

Nicolas Garcelon  1   2 , Antoine Neuraz  1   2 , Vincent Benoit  1 , Rémi Salomon  1   3 , Anita Burgun  2   4
Affiliations

Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse

Nicolas Garcelon et al. J Am Med Inform Assoc. .

Abstract

Objective: The repurposing of electronic health records (EHRs) can improve clinical and genetic research for rare diseases. However, significant information in rare disease EHRs is embedded in the narrative reports, which contain many negated clinical signs and family medical history. This paper presents a method to detect family history and negation in narrative reports and evaluates its impact on selecting populations from a clinical data warehouse (CDW).

Materials and methods: We developed a pipeline to process 1.6 million reports from multiple sources. This pipeline is part of the load process of the Necker Hospital CDW.

Results: We identified patients with "Lupus and diarrhea," "Crohn's and diabetes," and "NPHP1" from the CDW. The overall precision, recall, specificity, and F-measure were 0.85, 0.98, 0.93, and 0.91, respectively.

Conclusion: The proposed method generates a highly accurate identification of cases from a CDW of rare disease EHRs.

Keywords: data warehouse; electronic health records; natural language processing; rare diseases; search engine.

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Figures

Figure 1.
Figure 1.
Illustration of the objective of our method and its context.
Figure 2.
Figure 2.
Overview of the pipeline.
Figure 3.
Figure 3.
Both the number of patients retrieved and the accuracy (represented by precision, recall, specificity, and F-measure) are presented without filtering, with family history detection and with both detection (family history and negation).
See this image and copyright information in PMC

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