ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Neurodevelopment
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1681152
This article is part of the Research TopicDeveloping brain in neonatal stage: mechanism, prediction and interventionView all articles
Differential Item Functioning in Neonatal Behavioral Neurological Assessment in Term High-Risk Infants in NICU Based on A Machine Learning Approach
Provisionally accepted
- Shenzhen Children's Hospital, Shenzhen, China
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Aim: This study adopted Rasch Analysis to evaluate the psychometric properties of the neonatal behavioural neurological assessment (NBNA) in high-risk infants during their NICU stay. Methods: A total of 543 infants (14.26±7.02 days of age) were included in the study. We used the Rasch Model (RM) to assess the reliability and validity of the NBNA and GPCMlasso models to examine differential item functioning (DIF). Results: The samples responded to the NBNA according to the Rasch Model pattern. We found that the NBNA measures neurobehavior with one extra component regarding visual reactions. We found items that displayed disorder category functions in the NBNA. Conservatively, we found that the participants ' responses to the NBNA items were mostly dependent on the neurological developmental level, regardless of demographic traits. Conclusion: Our results support the applicability of the NBNA in depicting neurobehaviors in high-risk NICU infants. We found that high-risk infants could respond to NBNA items that were mostly dependent on the neural developmental level. The category functioning analysis revealed that the items provided inaccurate information owing to the disordered rating design.
Keywords: NBNA, Rasch model, Differential Item Functioning, High-risk infant, machine learning
Received: 07 Aug 2025; Accepted: 22 Sep 2025.
Copyright: © 2025 Peng, Tan, Wang, Feng, Feng and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Kanglong Peng, 18096723g@connect.polyu.hk
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