ArticleFrontiers in psychology2025
Personalized recommendation algorithm for rehabilitation intervention in children with autism spectrum disorder based on the cognitive diagnosis model.
Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aim: This study applied the Cognitive Diagnostic Model (CDM) to develop a personalized recommendation algorithm for rehabilitation intervention in children and adolescents with autism spectrum disorder (ASD). Methods: A total of 3,319 children and adolescents were included. Model selections recommended the Generalized Deterministic Input, Noisy "Or" Gate Model (GDINA), to simulate the response pattern of participants in the Autism Behavior Checklist. Results: Both absolute and relative indices confirmed that the response pattern of the participants displayed acceptable fitness to GDINA. Twenty-eight symptom modalities were identified, but only 12 were assigned to over one percent of this sample. Language dysfunction is commonly observed. A diagram of the possible developmental trajectory of participants with ASD indicates that sensory and related functions can be primary targets for those with severe autistic symptoms. One possible rehabilitation route was identified in this diagram that involved 2,621 participants. A detailed personalized analysis was demonstrated in randomly selected cases from this sample. Conclusion: Our study developed a personalized recommended algorithm using CDM in designing individualized interventions for children and adolescents with ASD. First, our results confirmed the heterogeneity of ASD symptoms. Importantly, the information derived from the CDM allowed for the construction of a possible development diagram of the functions defined by ABC. Although these results are theoretically sound and reasonable, they remain data-driven. Further empirical validation, particularly through experience with rigorous design, is necessary to confirm the alignment between real-world practices and data-driven models.
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