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

ABSTRACT:

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 prioritize patients for WGS.

Methods

Institutional databases of electronic health records (EHRs) are logical starting points for identifying patients with undiagnosed Mendelian diseases. We have developed automated means to prioritize patients for rapid and whole genome sequencing (rWGS and WGS) directly from clinical notes. Our approach combines a clinical natural language processing (CNLP) workflow with a machine learning-based prioritization tool named Mendelian Phenotype Search E

SUBMITTER: Peterson B 

PROVIDER: S-EPMC10018992 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature