ArticleBMC medical informatics and decision making2019
Rare disease knowledge enrichment through a data-driven approach.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Synonym Augmentation for Rare Disease Identification in Unstructured Data.medRxiv : the preprint server for health sciences · 2026Article
- Effects of continuing medical education on emergency trainees' rare disease knowledge and attitude: a single-center study.BMC medical education · 2025Article
- Machine learning in rare disease.Nature methods · 2023Review
- OARD: Open annotations for rare diseases and their phenotypes based on real-world data.American journal of human genetics · 2022Article
- Multi-faceted semantic clustering with text-derived phenotypes.Computers in biology and medicine · 2021Article
- Towards similarity-based differential diagnostics for common diseases.Computers in biology and medicine · 2021Article
- Generalized and transferable patient language representation for phenotyping with limited data.Journal of biomedical informatics · 2021Article
- [Extraction of features from clinical routine data using text mining].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2021Article
- The case for open science: rare diseases.JAMIA open · 2020Review
- Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization.Methods of information in medicine · 2020Article
- Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook.Yearbook of medical informatics · 2020Review
- Review
- Review
- HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.Journal of biomedical informatics · 2019Article
- Gene- and Disease-Based Expansion of the Knowledge on Inborn Errors of Immunity.Frontiers in immunology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundExisting 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).
methodsWe 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.
resultsWe 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.
conclusionsPer 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.