Related Experiment Video
Updated: Jul 12, 2026
Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Published on: August 25, 2014
Harnessing the power of child development records to detect early neurodevelopmental disorders using Bayesian
Yuhei Hatakenaka1,2,3, Koutaro Hachiya4, Jakob Åsberg Johnels3
1Faculty of Humanities and Sociologies, University of the Ryukyus, Okinawa, Japan.
Insights
Public health nurses can use developmental data to identify early indicators of neurodevelopmental disorders (NDDs) in children. An 18-month checkup accurately identified children at NDD risk, aiding early intervention.
Area of Science:
- Pediatrics
- Developmental Psychology
- Public Health
Background:
- Early detection of neurodevelopmental disorders (NDDs) is crucial for timely intervention and improved outcomes.
- Developmental data collected by public health nurses (PHNs) offers a valuable resource for identifying at-risk children.
- Bayesian network (BN) analysis can enhance diagnostic accuracy by identifying complex factor combinations.
Purpose of the Study:
- To analyze developmental data from PHNs to identify early indicators of NDDs in young children.
- To utilize BN analysis to determine factor combinations that improve NDD diagnosis accuracy.
- To assess the predictive accuracy of developmental assessments at different child ages.
Main Methods:
- Analysis of developmental data from 501 children at 18 and 36-month checkups.
- Inclusion of demographic, pregnancy, delivery, neonatal, maternal, and physical/neurological factors.
- Bayesian network analysis and receiver operating characteristic (ROC) curve analysis for predictive accuracy.
Main Results:
- Several infant/toddler factors, including meconium-stained amniotic fluid, 1-minute Apgar score, and early developmental milestones, were significantly associated with NDD diagnoses.
- The 18-month checkup demonstrated accurate identification of children at NDD risk.
- While the 10-month checkup was useful for screening, the 18-month evaluation provided more reliable risk identification.
Conclusions:
- Developmental records hold significant potential for early NDD detection.
- Early monitoring and intervention are vital for children identified as at-risk for NDDs.
- Findings can inform community-based infant mental health initiatives.
Aim:
This study aims to analyse the developmental data from public health nurses (PHNs) to identify early indicators of neurodevelopmental disorders (NDDs) in young children using Bayesian network (BN) analysis to determine factor combinations that improve diagnosis accuracy.
Methods:
The study cohort was 501 children who underwent health checkups at 18 and 36-month. Data included demographics, pregnancy, delivery, neonatal factors, maternal interviews, and physical and neurological findings. Diagnoses were made by paediatricians and child psychiatrists using standardised tools. Predictive accuracy was assessed by the receiver operating characteristic (ROC) curve analysis.
Results:
We identified several infant/toddler factors significantly associated with NDD diagnoses. Predictive factors included meconium-stained amniotic fluid, 1 min Apgar score, and early developmental milestones. ROC curve analysis showed varying predictive accuracies based on evaluation timing. The 10-month checkup was valid for screening but less reliable for excluding low-risk cases. The 18-month evaluation accurately identified children at NDD risk.
Conclusion:
The study demonstrates the potential of using developmental records for early NDD detection, emphasising early monitoring and intervention for at-risk children. These findings could guide future infant mental health initiatives in the community.
More Related Videos
11:14A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
Published on: October 4, 2015
10:47Rapid Detection of Neurodevelopmental Phenotypes in Human Neural Precursor Cells NPCs
Published on: March 2, 2018
