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.

Deep phenotyping unstructured data mining in an extensive pediatric database to unravel a common KCNA2 variant in neurodevelopmental syndromes / Hully, Marie; Lo Barco, Tommaso; Kaminska, Anna; Barcia, Giulia; Cances, Claude; Mignot, Cyril; Desguerre, Isabelle; Garcelon, Nicolas; Kabashi, Edor; Nabbout, Rima. - In: GENETICS IN MEDICINE. - ISSN 1098-3600. - 23:5(2021), pp. 968-971. [10.1038/s41436-020-01039-z]

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

Lo Barco, Tommaso;
2021

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.
2021
23
5
968
971
Deep phenotyping unstructured data mining in an extensive pediatric database to unravel a common KCNA2 variant in neurodevelopmental syndromes / Hully, Marie; Lo Barco, Tommaso; Kaminska, Anna; Barcia, Giulia; Cances, Claude; Mignot, Cyril; Desguerre, Isabelle; Garcelon, Nicolas; Kabashi, Edor; Nabbout, Rima. - In: GENETICS IN MEDICINE. - ISSN 1098-3600. - 23:5(2021), pp. 968-971. [10.1038/s41436-020-01039-z]
Hully, Marie; Lo Barco, Tommaso; Kaminska, Anna; Barcia, Giulia; Cances, Claude; Mignot, Cyril; Desguerre, Isabelle; Garcelon, Nicolas; Kabashi, Edor; Nabbout, Rima
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S1098360021014325-main.pdf

Open access

Tipologia: Versione pubblicata dall'editore
Dimensione 715.29 kB
Formato Adobe PDF
715.29 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1290170
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 8
social impact