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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...
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 |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
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 |
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