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. 2019 Feb 14;19(1):32.
doi: 10.1186/s12911-019-0752-9.

Rare disease knowledge enrichment through a data-driven approach

Feichen Shen  1 , Yiqing Zhao  2 , Liwei Wang  2 , Majid Rastegar Mojarad  2 , Yanshan Wang  2 , Sijia Liu  2 , Hongfang Liu  3
Affiliations

Rare disease knowledge enrichment through a data-driven approach

Feichen Shen et al. BMC Med Inform Decis Mak. .

Abstract

Background: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR).

Methods: We first applied association rule mining algorithms on EMR to extract significant phenotype-disease associations and enriched existing rare disease resources (Human Phenotype Ontology and Orphanet (HPO-Orphanet)). We generated phenotype-disease bipartite graphs for HPO-Orphanet, EMR, and enriched knowledge base HPO-Orphanet + and conducted a case study on Hodgkin lymphoma to compare performance on differential diagnosis among these three graphs.

Results: We used disease-disease similarity generated by the eRAM, an existing rare disease encyclopedia, as a gold standard to compare the three graphs with sensitivity and specificity as (0.17, 0.36, 0.46) and (0.52, 0.47, 0.51) for three graphs respectively. We also compared the top 15 diseases generated by the HPO-Orphanet + graph with eRAM and another clinical diagnostic tool, the Phenomizer.

Conclusions: Per our evaluation results, our approach was able to enrich existing rare disease knowledge resources with phenotype-disease associations from EMR and thus support rare disease differential diagnosis.

Keywords: Data-driven approach; Differential diagnosis; Knowledge enrichment; Rare disease.

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Conflict of interest statement

Ethics approval and consent to participate

This study used existing records to conduct a retrospective study. The study and a waiver of informed consent were approved by Mayo Clinic Institutional Review Board in accordance with 45 CFR 46.116 (Approval #17–003030).

Consent for publication

The author(s) declare(s) that the manuscript does not contain any individual person’s data. So this paper requires no consent to publish.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
System workflow
Fig. 2
Fig. 2
Plotted curve between association ranking and two metrics
Fig. 3
Fig. 3
Characterization of associations
Fig. 4
Fig. 4
Comparison on differential diagnostic suggestion performance for Hodgkin Lymphoma
Fig. 5
Fig. 5
Interactive web-based tool for differential diagnostic suggestion (CD stands for Common Disease, and RD stands for Rare Disease)
See this image and copyright information in PMC

References

    1. Boat TF, Field MJ. Rare diseases and orphan products: accelerating research and development. Washington, D.C.: National Academies Press; 2011. - PubMed
    1. Survey of the delay in diagnosis for 8 rare diseases in Europe. Available at: https://wwweurordisorg/sites/default/files/publicationsFact_Sheet_Eurordiscare2pdf.
    1. Rare Diseases Difficult to Diagnose, Cures Hard to Come By. Available at: https://news.aamc.org/research/article/rare-diseases-difficult-diagnose-....
    1. Phenotype Definition. Available at: http://medical-dictionary.thefreedictionary.com/phenotype.
    1. Hodgkin Lymphoma Differential Diagnosis. Available at: https://emedicine.medscape.com/article/201886-differential.

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