ArticleFrontiers in genetics2018
RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 46 citations in OpenAlex.
- A Multimodal Biomedical Transformer Fusion Network for Disease-Level Rare-Disease-Inheritance Classification Using Ontology-Enriched Text, Metadata, and Gene Associations.Biomedicines · 2026Article
- Reframing AI for Rare Disease Recognition.Research square · 2026Article
- GEN-KnowRD: Reframing AI for Rare Disease Recognition.medRxiv : the preprint server for health sciences · 2026Article
- A labeled medical records corpus for the timely detection of rare diseases using machine learning approaches.Scientific reports · 2025Article
- A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs.NPJ digital medicine · 2025Article
- Identification of Smith-Magenis syndrome cases through an experimental evaluation of machine learning methods.Frontiers in computational neuroscience · 2024Article
- Enhancing diagnosis of Hirschsprung's disease using deep learning from histological sections of post pull-through specimens: preliminary results.Pediatric surgery international · 2023Article
- [Faster diagnosis of rare diseases with artificial intelligence-A precept of ethics, economy and quality of life].Innere Medizin (Heidelberg, Germany) · 2023Review
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- An artificial intelligence-based approach for identifying rare disease patients using retrospective electronic health records applied for Pompe disease.Frontiers in neurology · 2023Article
- PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care.BMC medical informatics and decision making · 2022Article
- New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches.International journal of molecular sciences · 2022Review
- A Visual Phenotype-Based Differential Diagnosis Process for Rare Diseases.Interdisciplinary sciences, computational life sciences · 2022Article
- Knowledge-based approaches to drug discovery for rare diseases.Drug discovery today · 2022Review
- The role of phenotype-based search approaches using public online databases in diagnostics of Mendelian disorders.Genetics in medicine : official journal of the American College of Medical Genetics · 2021Article
- Diagnosis support systems for rare diseases: a scoping review.Orphanet journal of rare diseases · 2020Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, paving the way for new disease diagnostics. However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outcome and the results showed that all models had a high diagnostic precision (≥98%) with the highest recall reaching up to 95% while the models with machine learning methods showed the best performance. To promote effective diagnosis for rare disease in clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases with the above four diagnostic models. The system is freely accessible through http://www.unimd.org/RDAD/.
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