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Updated: Jul 7, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

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Published on: October 4, 2015

Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods.

Andrea K Bowe¹, Gordon Lightbody^1,2, Anthony Staines³

¹INFANT Research Centre, University College Cork, Cork, Ireland.

JAMA Network Open

|December 26, 2023

Summary

This summary is machine-generated.

A predictive model using routine data can identify very preterm infants at high risk for cognitive delay. This allows for early, targeted interventions to improve cognitive outcomes in this vulnerable population.

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Last Updated: Jul 7, 2025

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Published on: October 4, 2015

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Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

Area of Science:

Neonatal Medicine
Developmental Pediatrics
Machine Learning in Healthcare

Background:

Early intervention is crucial for improving cognitive outcomes in very preterm infants.
Identifying infants most in need of intervention is essential due to resource limitations.

Purpose of the Study:

To evaluate a predictive model for cognitive delay at 2 years of age in very preterm infants.
The model utilizes routinely available clinical and sociodemographic data.

Main Methods:

A prognostic study using the Swedish Neonatal Quality Register (2015-2022).
Machine learning models were trained to predict cognitive delay (Bayley Scales score <90) at 2 years corrected age.
Included infants born <32 weeks gestational age, excluding major congenital anomalies.

Main Results:

A logistic regression model with 26 features predicted cognitive delay with an area under the receiver operating curve of 0.77.
Key predictors included non-Scandinavian family language, prolonged hospitalization, low birth weight, discharge destination, and lack of breastfeeding.
The model achieved high sensitivity (0.93) in identifying infants with cognitive delay.

Conclusions:

Predictive modeling in neonatal care can facilitate early and targeted interventions.
This approach can help identify very preterm infants at highest risk for cognitive impairment.
Optimizing interventions based on predicted risk can improve developmental trajectories.