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Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A
Tianyi Zhou1,2, Yaojia Shen1,2, Jinlang Lyu1,2
1Department of Maternal and Child Health, School of Public Health, Peking University, Beijing 100191, China.
Insights
Predicting infant neurodevelopmental abnormality is key for early intervention. This study used maternal data and machine learning to identify risks before age one, finding medication exposure during pregnancy is a significant factor.
Area of Science:
- Pediatric Neurodevelopment
- Machine Learning in Healthcare
- Pregnancy and Child Development
Background:
- Early identification of neurodevelopmental abnormalities in infants is critical for timely intervention and improved outcomes.
- Predictive modeling offers a promising approach to identify at-risk infants before significant developmental delays occur.
- Maternal factors during pregnancy, including sociodemographics, behaviors, and medication use, may influence infant neurodevelopment.
Purpose of the Study:
- To develop and validate a predictive model for identifying infants with neurodevelopmental abnormalities before one year of age.
- To utilize interpretable machine learning to understand the influence of maternal factors on infant neurodevelopment.
- To assess the predictive value of maternal medication exposure during pregnancy on offspring neurodevelopmental outcomes.
Main Methods:
- Development of artificial neural network models using maternal sociodemographic, behavioral, and medication-usage data.
- Application of interpretable machine learning to identify key predictive variables.
- Evaluation of model performance using metrics such as Area Under the Curve (AUC), sensitivity, specificity, and accuracy.
Main Results:
- The predictive models demonstrated good efficacy in identifying neurodevelopmental abnormalities, particularly in fine motor and problem-solving areas (AUCs up to 0.670 and 0.643, respectively).
- The overall model for any neurodevelopmental abnormality achieved strong performance with an AUC of 0.821, sensitivity of 0.700, and accuracy of 0.721.
- Maternal exposure to medications like acetaminophen, ferrous succinate, and midazolam during pregnancy was identified as a significant factor influencing specific areas of infant neurodevelopment.
Conclusions:
- Predictive models utilizing maternal information can effectively identify infants at risk for neurodevelopmental abnormalities before one year of age.
- Maternal medication exposure during pregnancy is a crucial, previously underestimated, predictor of offspring neurodevelopmental outcomes.
- This study highlights the potential of machine learning in uncovering critical prenatal risk factors for early childhood neurodevelopment.
Abstract:
Early identification of children with neurodevelopmental abnormality is a major challenge, which is crucial for improving symptoms and preventing further decline in children with neurodevelopmental abnormality. This study focuses on developing a predictive model with maternal sociodemographic, behavioral, and medication-usage information during pregnancy to identify infants with abnormal neurodevelopment before the age of one. In addition, an interpretable machine-learning approach was utilized to assess the importance of the variables in the model. In this study, artificial neural network models were developed for the neurodevelopment of five areas of infants during the first year of life and achieved good predictive efficacy in the areas of fine motor and problem solving, with median AUC = 0.670 (IQR: 0.594, 0.764) and median AUC = 0.643 (IQR: 0.550, 0.731), respectively. The final model for neurodevelopmental abnormalities in any energy region of one-year-old children also achieved good prediction performance. The sensitivity is 0.700 (IQR: 0.597, 0.797), the AUC is 0.821 (IQR: 0.716, 0.833), the accuracy is 0.721 (IQR: 0.696, 0.739), and the specificity is 0.742 (IQR: 0.680, 0.748). In addition, interpretable machine-learning methods suggest that maternal exposure to drugs such as acetaminophen, ferrous succinate, and midazolam during pregnancy affects the development of specific areas of the offspring during the first year of life. This study established predictive models of neurodevelopmental abnormality in infants under one year and underscored the prediction value of medication exposure during pregnancy for the neurodevelopmental outcomes of the offspring.
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