Predicting autism traits from baby wellness records: A machine learning approach
Ayelet Ben-Sasson1, Joshua Guedalia1, Keren Ilan1
1University of Haifa, Israel.
Summary
Early autism spectrum condition (ASC) identification is crucial for intervention. A new model uses baby wellness visit records from the first two years to predict ASC likelihood, aiding early detection in boys and girls.
Area of Science:
- Developmental Pediatrics
- Computational Health
- Public Health
Background:
- Early identification of autism spectrum conditions (ASC) is vital for effective intervention.
- Routine health data offers potential for detecting early ASC indicators.
- Technological advancements facilitate novel approaches to developmental screening.
Purpose of the Study:
- To develop and test a predictive model for autism spectrum condition (ASC) likelihood using routinely collected baby wellness visit records.
- To assess the model's performance in identifying ASC in children within the first two years of life.
- To explore sex-specific patterns in early ASC detection using health records.
Main Methods:
- A predictive model was developed using electronic health records from baby wellness visits for children up to 2 years old.
- The study included a large cohort: 591,989 non-autistic children and 12,846 children diagnosed with ASC.
- Model performance was evaluated for identifying ASC, with analyses considering sex-specific differences.
Main Results:
- The model identified two-thirds of children with ASC (63% of boys, 66% of girls).
- Key predictive features included language, fine motor, and social milestones (12-24 months), maternal age, and growth patterns.
- Parental concerns about development or hearing were significant predictors, alongside variations in birth and growth parameters between models.
Conclusions:
- Routinely collected health data during the first two years of life can be leveraged to support early autism spectrum condition (ASC) detection.
- Developed models show promise in identifying early signs of ASC in both boys and girls.
- These findings support the integration of predictive analytics into standard pediatric care for timely ASC identification and intervention.
Related Concept Videos
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
01:19Autism Spectrum Disorder
83
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
83
01:18Steps in Outbreak Investigation
123
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
123