Development of a Prediction Model to Identify Children at Risk of Future Developmental Delay at Age 4 in a Population-Based Setting

Dokkum, Nienke H. van; Reijneveld, Sijmen A.; Heymans, Martijn W.; Bos, Arend F.; Kroon, Marlou L. A. de

doi:10.3390/ijerph17228341

Cited by 4 publications

(12 citation statements)

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“…A disadvantage is that other than producing sensitivity and specificity values, the underlying concepts and results interpretation are not familiar to the majority of medical and social science researchers. The results of the RF modelling here showed remarkably high sensitivity and specificity which were far in excess of existing regression-based algorithms predicting developmental delay [15]. The features with the highest importance scores: birthweight, household income, maternal and paternal ages and duration of labour can all be discerned at birth.…”

Section: Discussionmentioning

confidence: 57%

“…Van Dokkum et al [15] used logistic regression to predict developmental delay at age four, producing an algorithm with 73% sensitivity and 80% specificity. Another promising linear modelling approach when there is a large number of predictor variables is principal component analysis (PCA).…”

Section: Of 12mentioning

confidence: 99%

“…However, both statistical techniques assume linear relationships between values of the predictor variable and the outcome: PCA is based on linear transformation using orthogonal matrices, and logistic regression assumes that the logodds of the relationship between each predictor and the outcome is linear. There is no reason to believe that predictors have linear associations with developmental delay: for example, birthweight has negative associations with developmental delay at both low and very high levels [15,22], and socioeconomic position may not be important for health outcomes above a threshold level [23]. For the present research, Random Forest (RF), which is an ensemble decision-tree classifier was chosen.…”

Section: Of 12mentioning

confidence: 99%

“…The present study applied RF to predict psychomotor developmental delay in 9-month-old infants using data on a wide array of factors in pregnancy, birth and early infancy. The intent was to achieve higher sensitivity and specificity than has been achieved in prior studies approaching similar problems using regression methods, which rarely have greater than 80% sensitivity [15].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants

Waynforth¹

2023

Preprint

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Psychomotor developmental delay in infants includes failure to acquire abilities such as sitting, walking, grasping objects and communication at the ages when most infants have acquired these abilities. Known risk factors include a large number of aspects of family environment, socioeconomic position, problems in pregnancy and birth, and maternal health. It is clinically useful to be able to screen for developmental delay so that healthcare interventions can be considered. The present research used machine learning (random forest) to create an algorithm predicting psychomotor delay in 9-month-old infants using information ascertainable at birth and in early infancy. The dataset was the UK longitudinal Millennium Cohort study. Fifty-two predictors measuring socioeconomic indicators, paternal, family and social support for the mother, beliefs about good parenting, maternal health, pregnancy and birth were included in the initial algorithm. Feature reduction showed that of the 52 variables, birthweight, family income and parents’ ages had the highest feature importance scores and could alone correctly predict developmental delay with over 99% sensitivity and 100% specificity. The relationships between delay and some of the predictors, particularly income, were nonlinear and complex. The results suggest that the risk of psychomotor developmental delay can be identified in early infancy using machine learning, and that the best predictors are factors present prior to birth. Surprisingly, the most important factors included in the present study did not include illnesses during pregnancy such as eclampsia and infections.

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Section: Discussionmentioning

confidence: 57%

Section: Of 12mentioning

confidence: 99%

Section: Of 12mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants

Waynforth¹

2023

Preprint

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“…Children's progress in achieving developmental milestones in infancy and childhood is dependent on a large number of factors. These include growth in utero, size at birth, maternal health, socioeconomic position, genetically inherited developmental patterns, and many family and social factors [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. This makes predicting developmental delay in advance so that steps can be taken to avoid it difficult, as there are so many potentially important causes, and the relative importance of each is not clear.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants

Waynforth

2023

Reprod. Med.

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Psychomotor developmental delay in infants includes failure to acquire abilities such as sitting, walking, grasping objects and communication at the ages when most infants have acquired these abilities. Known risk factors include a large number of aspects of family environment, socioeconomic position, problems in pregnancy and birth and maternal health. It is clinically useful to be able to screen for developmental delay so that healthcare interventions can be considered. The present research used machine learning (random forest) to create an algorithm predicting psychomotor delay in 9-month-old infants using information ascertainable at birth and in early infancy. The dataset was the UK longitudinal Millennium Cohort study. In total, 53 predictors measuring socioeconomic indicators, paternal, family and social support for the mother, beliefs about good parenting, maternal health, pregnancy and birth were included in the initial algorithm. Feature reduction showed that of the 53 variables, birthweight, gestational age at birth, pre-pregnancy BMI, family income and parents’ ages had the highest feature importance scores and could alone correctly predict developmental delay with over 99% sensitivity and 100% specificity. No features measuring aspects of early infant care or environment meaningfully added to algorithm performance. The relationships between delay and some of the predictors, particularly income, were nonlinear and complex. The results suggest that the risk of psychomotor developmental delay can be identified in early infancy using machine learning, and that the best predictors are factors present prior to and at birth.

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Predicting autism traits from baby wellness records: A machine learning approach

Ben-Sasson,

Guedalia,

Ilan

et al. 2024

Autism

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Early detection of autism spectrum condition is crucial for children to maximally benefit from early intervention. The study examined a machine learning model predicting the increased likelihood for autism from wellness records from 0 to 24 months. The study included 591,989 non-autistic and 12,846 autistic children. A gradient boosting model with a threefold cross-validation and SHAPley additive explanation tool quantified feature importance. The model had an average area under the curve of 0.81 ( SD = 0.004). The high-likelihood group detected by the model had a 0.073 autism spectrum condition incidence rate; 3.42-fold more than in the entire cohort (0.02). Sex-specific models had higher specificity (0.81 boys and 0.79 girls) than sensitivity (0.64 boys and 0.66 girls). The common predictors were more parental concerns, older mothers, never nursing, lower initial and higher last weight percentiles, and several delayed milestones. SHAPley additive explanation tool results show common, important predictors in the full sample and separate boys’ and girls’ models. These included birth, growth, familial, postnatal parameters and delayed language, fine motor, and social milestones from 12 to 24 months. Machine learning algorithms can help detect increased autism signs by relying on the multidimensional data routinely recorded during the first 2 years. Lay abstract Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life. It included records of 591,989 non-autistic children and 12,846 children with autism. The model identified two-thirds of the autism spectrum condition group (boys 63% and girls 66%). Sex-specific models had several predictive features in common. These included language development, fine motor skills, and social milestones from visits at 12–24 months, mother’s age, and lower initial growth but higher last growth measurements. Parental concerns about development or hearing impairment were other predictors. The models differed in other growth measurements and birth parameters. These models can support the detection of early signs of autism in girls and boys by using information routinely recorded during the first 2 years of life.

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Development of a Prediction Model to Identify Children at Risk of Future Developmental Delay at Age 4 in a Population-Based Setting

Cited by 4 publications

References 35 publications

A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants

A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants

A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants

Predicting autism traits from baby wellness records: A machine learning approach

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