Deep phenotyping unstructured data mining in an extensive pediatric database to unravel a common KCNA2 variant in neurodevelopmental syndromes

Abstract

Purpose: Electronic health records are gaining popularity to detect and propose interdisciplinary treatments for patients with similar medical histories, diagnoses, and outcomes. These files are compiled by different nonexperts and expert clinicians. Data mining in these unstructured data is a transposable and sustainable methodology to search for patients presenting a high similitude of clinical features. 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.