Cluster analysis and visualisation of electronic health records data to identify undiagnosed patients with rare genetic diseases.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
01 Mar 2024
01 Mar 2024
Historique:
received:
01
11
2023
accepted:
23
02
2024
medline:
1
3
2024
pubmed:
1
3
2024
entrez:
29
2
2024
Statut:
epublish
Résumé
Rare genetic diseases affect 5-8% of the population but are often undiagnosed or misdiagnosed. Electronic health records (EHR) contain large amounts of data, which provide opportunities for analysing and mining. Data mining, in the form of cluster analysis and visualisation, was performed on a database containing deidentified health records of 1.28 million patients across 3 major hospitals in Singapore, in a bid to improve the diagnostic process for patients who are living with an undiagnosed rare disease, specifically focusing on Fabry Disease and Familial Hypercholesterolaemia (FH). On a baseline of 4 patients, we identified 2 additional patients with potential diagnosis of Fabry disease, suggesting a potential 50% increase in diagnosis. Similarly, we identified > 12,000 individuals who fulfil the clinical and laboratory criteria for FH but had not been diagnosed previously. This proof-of-concept study showed that it is possible to perform mining on EHR data albeit with some challenges and limitations.
Identifiants
pubmed: 38424111
doi: 10.1038/s41598-024-55424-8
pii: 10.1038/s41598-024-55424-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5056Subventions
Organisme : National Medical Research Council,Singapore
ID : NMRC/CSAINV21jun-0003
Informations de copyright
© 2024. The Author(s).
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