Rare disease knowledge enrichment through a data-driven approach

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. Electronic supplementary material The online version of this article (10.1186/s12911-019-0752-9) contains supplementary material, which is available to authorized users.

Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

“…After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Five reviewers reviewed these pre-selected papers to best four best papers [4][5][6][7]. In direct line with the research presented last year [1], the four best papers published in 2019 demonstrated even further the added-value of ontology-based data integration approaches and that the development of ontology methods is an active area of bioinformatics research.…”

“…Section editors achieved a first selection of 148 papers based on titles and abstracts. After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 . Five reviewers reviewed these pre-selected papers to best four best papers 4 5 6 7 .…”

“…This EHR-based phenomics approach, applied on the data of 15 million individuals, is an important step towards the international use of UK EHR data for health research, with applications for translational research at the population level. Also selected as a best paper for 2019, Shen et al, [7] describe a work which aims at enriching available rare disease resources by mining phenotype-disease associations from a 5-year collection of 12.8 million clinical notes from electronic medical records at the Mayo Clinic. Their approach was able to enrich existing rare disease knowledge resources with phenotype-disease associations, with an application to the differential diagnosis across rare and non-rare diseases.…”

Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives—a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

“…A recent study discusses combining existing rare disease knowledge resources and data within the electronic medical record to assist in identifying rare disease phenotypes. 186 In addition, identification of naturally occurring disease models in companion animals that are representative of rare human diseases can result in translation therapies that improve both animal and human lives. For example, osteosarcoma (OSA) is a type of bone cancer that while the most common bone tumor in humans, is very rare with <1000 new cases diagnosed in the United States each year and half of those diagnosed are under the age of 25.…”

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

“…Additionally, a DDSS based on phenotypic information extraction using collaborative filtering in combination with natural language processing (NLP) was created. Firstly, it was applied to an EHR database encompassing multiple RDs, afterwards being improved by the addition of literature mining [37,38]. Ada XD is a promising DDSS prototype using the augmented Quick Medical Reference (QMR) Bayesian network with an incorporated probabilistic reasoning engine.…”