Deep phenotyping unstructured data mining in an extensive pediatric database to unravel a common KCNA2 variant in neurodevelopmental syndromes.
Hully M, Lo Barco T, Kaminska A, Barcia G, Cances C, Mignot C, Desguerre I, Garcelon N, Kabashi E, Nabbout R.
Source : Genet Med
2021 Jan 26
Pmid : 33500571
METHODS: Exome and targeted next-generation sequencing bioinformatics analyses were performed at the Imagine Institute. Similarity Index (SI), an algorithm based on a vector space model (VSM) that exploits concepts extracted from clinical narrative reports was used to identify patients with highly similar clinical features.
RESULTS: Here we describe a case of "automated diagnosis" indicated by Dr. Warehouse, a biomedical data warehouse oriented toward clinical narrative reports, developed at Necker Children's Hospital using around 500,000 patients' records. Through the use of this warehouse, we were able to match and identify two patients sharing very specific clinical neonatal and childhood features harboring the same de novo variant in KCNA2.
CONCLUSION: This innovative application of database clustering clinical features could advance identification of patients with rare and common genetic conditions and detect with high accuracy the natural history of patients harboring similar genetic pathogenic variants.