Using deep learning and electronic health records to detect Noonan syndrome in pediatric patients
Open accessAbstract
PURPOSE: The variable expressivity and multisystem features of Noonan syndrome (NS) make it difficult for patients to obtain a timely diagnosis. Genetic testing can confirm a diagnosis, but underdiagnosis is prevalent owing to a lack of recognition and referral for testing. Our study investigated the utility of using electronic health records (EHRs) to identify patients at high risk of NS. METHODS: Using diagnosis texts extracted from Cincinnati Children's Hospital's EHR database, we constructed deep learning models from 162 NS cases and 16,200 putative controls. Performance was evaluated on 2 independent test sets, one containing patients with NS who were previously diagnosed and the other containing patients with undiagnosed NS. RESULTS: Our novel method performed significantly better than the previous method, with the convolutional neural network model achieving the highest area under the precision-recall curve in both test sets (diagnosed: 0.43, undiagnosed: 0.16). CONCLUSION: The results suggested the validity of using text-based deep learning methods to analyze EHR and showed the value of this approach as a potential tool to identify patients with features of rare diseases. Given the paucity of medical geneticists, this has the potential to reduce disease underdiagnosis by prioritizing patients who will benefit most from a genetics referral.
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Deep learning/artificial intelligence searches have been successful in finding undiagnosed patients with genetic conditions.
“In addition to algorithm-driven electronic health record searches, deep learning/artificial intelligence searches have been successful in finding undiagnosed patients with genetic conditions.102”
From Clinical practice guidelines for the care of girls and women with Turner syndrome. (2024) · cites this paper as [102]
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Clinical practice guidelines for the care of girls and women with Turner syndrome.
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- Authors
- Yang, Z., Shikany, A., Ni, Y., Zhang, G., Weaver, K. N., & Chen, J.
- Affiliations
- Cincinnati Children's Hospital Medical Center (US)
- Corresponding author
- Chen, Jing
- Venue
- Genetics in Medicine
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 24
- Issue
- 11
- Pages
- 2329-2337
- ISSN
- 1098-3600 (ISSN-L)
- OpenAlex
- W4295402507
- Cited by
- 20
- References
- 30
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 7 · 2023: 4
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Genetics in Medicine (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Patient-Centered Outcomes Research Institute (RI-CHOP-01-PS1)
- Cincinnati Children's Hospital Medical Center
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- Noonan syndrome, electronic health records, deep learning, convolutional neural networks, rare disease detection, underdiagnosis, pediatric patients
- MeSH terms
- Deep Learning, Child, Genetic Testing, Humans, Noonan Syndrome, Databases, Factual, Electronic Health Records
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