Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning

BACKGROUND: Rapidly and efficiently identifying critically ill infants for whole genome sequencing (WGS) is a costly and challenging task currently performed by scarce, highly trained experts and is a major bottleneck for application of WGS in the NICU. There is a dire need for automated means to pr...

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Detalles Bibliográficos
Autores principales: Peterson, Bennet, Hernandez, Edgar Javier, Hobbs, Charlotte, Malone Jenkins, Sabrina, Moore, Barry, Rosales, Edwin, Zoucha, Samuel, Sanford, Erica, Bainbridge, Matthew N., Frise, Erwin, Oriol, Albert, Brunelli, Luca, Kingsmore, Stephen F., Yandell, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018992/
https://www.ncbi.nlm.nih.gov/pubmed/36927505
http://dx.doi.org/10.1186/s13073-023-01166-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018992/
https://www.ncbi.nlm.nih.gov/pubmed/36927505
http://dx.doi.org/10.1186/s13073-023-01166-7

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