ArticleNPJ digital medicine2025
Information content as a health system screening tool for rare diseases.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- A Multimodal Biomedical Transformer Fusion Network for Disease-Level Rare-Disease-Inheritance Classification Using Ontology-Enriched Text, Metadata, and Gene Associations.Biomedicines · 2026Article
- Reimagining care of people living with rare diseases with artificial intelligence.PLoS medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Rare diseases (RD) affect 3.5-5.9% of the global population, yet patients often endure a diagnostic odyssey averaging around 5-6 years, navigating multiple specialties and referrals. This study investigates the use of information content (IC) as a screening tool to identify RD candidates using SNOMED CT, the clinical terminology standard for the electronic health record (EHR) in the Singapore health system. A longitudinal dataset of 1,274,199 patients (2018-2022) containing 35,898 unique SNOMED terms was analyzed. Rare disease patients (n = 17,575) were identified through SNOMED-Orphanet mappings. IC effectively distinguished RD patient profiles from the first clinical encounter by applying outcome-driven thresholds. The simplicity of implementing the proposed method enables a screening test that can achieve ~95% sensitivity, while maintaining the potential follow-up burden at a reasonable level (20% precision starting from 3 encounters, with an IC threshold of 8.17). This approach also surfaced 71 underdiagnosed rare diseases in the population (57 of genetic origin). To our knowledge, this is the first study to apply information-theoretic metrics to EHR for RD screening, demonstrating its potential as a screening tool at both hospital and health system levels.
Identifiers
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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.