ScreeningPaL: LLM-NLP Enabled Early Autism Detection Method from Caregiver's Free-Text Input
Sumaiya Afroz Mila1, Jeba Maliha2, Md Rafiul Kabir2
1University of Florida, Gainesville, FL.
Summary
Early autism risk detection is improved using advanced natural language processing on caregiver reports. This text-driven method offers a low-cost, accessible approach for proactive developmental health monitoring.
Area of Science:
- Developmental Pediatrics
- Computational Linguistics
- Artificial Intelligence in Healthcare
Background:
- Early identification of autism spectrum disorder (ASD) traits is crucial for improving long-term quality of life.
- Current screening methods may lack accessibility or require specialized tools like speech samples.
- Caregiver-reported behavioral descriptions offer rich, albeit unstructured, data for potential early risk assessment.
Purpose of the Study:
- To develop and evaluate a text-driven approach for early autism risk detection using natural language processing (NLP).
- To assess the performance of advanced language models in analyzing free-text behavioral descriptions.
- To explore the impact of data augmentation on model generalization and performance.
Main Methods:
- Utilized synthetic free-text data generated from validated screening items to train language models.
- Employed fine-tuned transformer models, comparing their performance against other models like GPT and Gemini, and traditional NLP baselines.
- Evaluated model generalization on an external benchmark dataset (TASD) under domain shift conditions.
- Investigated the effect of augmenting training data with noisy, realistic text.
Main Results:
- Fine-tuned transformer models achieved 90% accuracy in early autism risk detection.
- These models outperformed GPT, Gemini, and conventional NLP baselines.
- Noise-aware data augmentation enhanced model performance, particularly improving recall in traditional pipelines.
- The methodology demonstrated effective generalization on an external dataset.
Conclusions:
- A text-driven NLP approach enables low-cost, accessible early autism risk assessment without structured questionnaires or speech samples.
- This method provides valuable early cues to support specialist evaluations and proactive developmental monitoring.
- Advanced language models, especially fine-tuned transformers, show significant promise for analyzing unstructured behavioral data in developmental screening.
