NeoGx: Machine-Recommended Rapid Genome Sequencing for Neonates.

ABSTRACT:

Background

Genetic disease is common in the Level IV Neonatal Intensive Care Unit (NICU), but neonatology providers are not always able to identify the need for genetic evaluation. We trained a machine learning (ML) algorithm to predict the need for genetic testing within the first 18 months of life using health record phenotypes.

Methods

For a decade of NICU patients, we extracted Human Phenotype Ontology (HPO) terms from clinical text with Natural Language Processing tools. Considering multiple feature sets, classifier architectures, and hyperparameters, we selected a classifier and made predictions on a validation cohort of 2,241 Level IV NICU admits born 2020-2021.

Results

Our classifier had ROC AUC of 0.87 and PR AUC of 0.73 when making predictions during the fir

SUBMITTER: Antoniou AA 

PROVIDER: S-EPMC11230343 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature