Autism Career Prediction Model Using Modular Machine Learning
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Solution Overview
Problem
Current methods lack an effective strategy for predicting suitable careers for autistic children, resulting in low employment rates and inadequate vocational training, as existing technologies fail to systematically assess and utilize data for personalized career guidance.
Innovation Solution
A method involving data collection and machine learning to construct an occupational prediction model using assessment data from autistic children, including developmental information, autism diagnosis, consonant articulation, social interaction, and executive function scores, to identify suitable occupations and evaluate rehabilitation program effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional assessment methods are used for autistic children, then the assessment process is simple, but the prediction accuracy of suitable careers is low
Solution Approach 1:
The patent segments the career prediction system into multiple independent modules: data collection module, feature extraction module, model training module, and prediction module. Each module processes specific aspects of assessment data (developmental information, autism diagnosis, consonant articulation, social interaction, executive function) separately, then integrates results to improve prediction accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent transforms qualitative assessment data into quantitative parameters by standardizing different assessment scales (M-CHAT, CARS, ABC, ADOS-2, BRIEF-2) into unified numerical features. This parameter transformation enables machine learning models to process diverse assessment information systematically, improving prediction precision through quantitative analysis of previously qualitative characteristics.
2Measurement precision
If comprehensive assessment data is collected from autistic children, then the career prediction accuracy improves, but the data collection time and cost increase
Solution Approach 1:
The patent implements preliminary action by collecting and storing comprehensive assessment data (developmental information, autism diagnosis results, consonant articulation scores, social interaction scores, executive function scores) during early childhood intervention stages. This data is stored in a structured database before career prediction is needed, eliminating the need for repeated data collection and reducing time loss when prediction is required.
Solution Approach 2:
The patent creates standardized data templates and assessment forms that can be reused across different children and assessment sessions. By establishing standardized data collection formats for various assessment scales, the system reduces redundant data collection efforts while maintaining comprehensive data quality for accurate career prediction.
3Stability of the object's composition
If standardized assessment scales are used for autistic children, then the assessment consistency improves, but the adaptability to individual children's needs decreases
Solution Approach 1:
The patent applies local quality by using standardized assessment scales for consistent data collection while allowing customized feature weighting and model parameter adjustment for each individual child. The system adapts to individual needs by selectively emphasizing certain assessment dimensions (e.g., social interaction for some children, executive function for others) based on their specific profiles, while maintaining overall assessment consistency through standardized methodologies.
Solution Approach 2:
The patent implements dynamics by making the assessment system adaptable through machine learning models that can adjust to individual children's characteristics. The system dynamically weights different assessment features based on each child's profile and uses iterative model training to improve individualized prediction accuracy while maintaining standardized data collection procedures for consistency.
Data Source
AI summary
A method to informationize the development of autistic children and predicting their future careers, comprising: collecting the assessment data C and occupational fitness value s of autistic children required by the occupational prediction model; constructing an occupational prediction model, assessment data C and occupational fitness value s are used to train the occupational prediction model, collecting the evaluation data C of the children to be predicted, and use the occupational prediction model to evaluate the occupational fitness value s of the children to be predicted, to obtain the suitable occupation for the children to be predicted. The present invention also discloses a system for implementing the above method. The invention also discloses a method and system for realizing the assessment of the effectiveness of a rehabilitation course and the recommendation of a course for a child with autism based on the above developmental course informatization and occupation prediction method.