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Related Concept Videos

Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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Autism Spectrum Disorder01:19

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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.
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Surveys02:16

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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  11. Enhancing Psychological Assessments With Open-ended Questionnaires And Large Language Models: An Asd Case Study.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Natural Language Processing
  11. Enhancing Psychological Assessments With Open-ended Questionnaires And Large Language Models: An Asd Case Study.

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Enhancing Psychological Assessments With Open-Ended Questionnaires and Large Language Models: An ASD Case Study.

Alberto Altozano, Maria Eleonora Minissi, Lucia Gomez-Zaragoza

    IEEE Journal of Biomedical and Health Informatics
    |August 15, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Large language models (LLMs) can automatically classify open-ended questionnaires for autism spectrum disorder (ASD) assessment. This approach offers a scalable and cost-effective method for psychological analysis in mental health research.

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    Area of Science:

    • Natural Language Processing
    • Computational Psychiatry
    • Machine Learning in Healthcare

    Background:

    • Open-ended questionnaires provide rich data but are difficult to analyze manually.
    • Advancements in Natural Language Processing (NLP) offer potential for automated analysis of qualitative data.
    • The application of NLP, specifically large language models (LLMs), in psychological classification remains underexplored.

    Purpose of the Study:

    • To propose and evaluate a methodology using pre-trained LLMs for the automatic classification of open-ended questionnaire responses.
    • To apply this methodology to classify autism spectrum disorder (ASD) using parental reports.
    • To compare different LLM training strategies and assess their performance and interpretability.

    Main Methods:

  • Utilized transcribed parental reports from 51 participants (26 typically developing, 25 with ASD).
  • Compared various LLM fine-tuning strategies, input representations, and specificity levels.
  • Derived subject-level predictions by aggregating responses from 12 individual questions, employing a voting system for final classification.
  • Conducted zero-shot evaluation using GPT-4o and assessed interpretability of different LLM approaches.

    • Main Results:

    • The best-performing approach achieved 84% subject-wise accuracy and 1.0 ROC-AUC.
    • This optimal strategy involved an OpenAI embedding model, per-question training, inclusion of questions in the input, and a voting system for aggregation.
    • Zero-shot evaluation with GPT-4o yielded comparable results, demonstrating the efficacy of both compact and large LLMs.
    • Interpretability varied, with locally deployable LLMs offering greater transparency than proprietary models.

    Conclusions:

    • LLMs provide a validated, scalable, and cost-effective method for automatically classifying open-ended questionnaires in psychological assessments.
    • This methodology shows significant promise for autism spectrum disorder (ASD) assessment and has broader applicability for analyzing qualitative data in mental health research.
    • A trade-off exists between the performance of proprietary LLMs and the interpretability offered by locally deployable models.