Predicting mental and psychomotor delay in very pre-term infants using machine learning.

ABSTRACT:

Background

Very preterm infants are at elevated risk for neurodevelopmental delays. Earlier prediction of delays allows timelier intervention and improved outcomes. Machine learning (ML) was used to predict mental and psychomotor delay at 25 months.

Methods

We applied RandomForest classifier to data from 1109 very preterm infants recruited over 20 years. ML selected key predictors from 52 perinatal and 16 longitudinal variables (1-22 mo assessments). SHapley Additive exPlanations provided model interpretability.

Results

Balanced accuracy with perinatal variables was 62%/61% (mental/psychomotor). Top predictors of mental and psychomotor delay overlapped and included: birth year, days in hospital, antenatal MgSO_4, days intubated, birth weight, abnormal crania

SUBMITTER: Demirci GM 

PROVIDER: S-EPMC10899098 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature