Scholarlink

<p>Alternating treatment designs (ATD) allow practitioners to rapidly compare the effectiveness of multiple interventions without needing long baseline phases. ATDs are typically evaluated using visual analysis and effect size analysis. However, past research suggests that different raters may come to different conclusions when analyzing these graphs. Furthermore, agreement between raters does not equate to accuracy. Artificial Intelligence (AI) technologies may improve the replicability of decision-making while performing adequately with smaller datasets. Within AI, Machine Learning (ML) algorithms can learn patterns and make data-driven predictions. These algorithms can quantify complex, intuitive patterns directly from data that would be difficult or impractical to define explicitly with traditional programming rules. This study investigates the use of ML technology to analyze ATD graphs. Specifically, the researchers examined which feature engineering techniques resulted in predictive performance for an ML model trained on simulated or non-simulated data and tested on non-simulated data. The best-performing models achieved classification accuracy above 90% with type 1 error rates below 15%. Deep neural networks (DNNs) led to the highest accuracy in detecting differentiated effects in ATD graphs while minimizing false positives. These results provide initial evidence that embedding DNNs into analysis of ATD single-case graphs could enhance the replicability of data analysis. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine Learning to Analyze Alternating Treatments Graphs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月31日

查看全文
  • Tobias Kausch,
  • Leslie Neely,
  • David Cox,
  • Katherine Holloway,
  • Adel Alaeddini

摘要

Alternating treatment designs (ATD) allow practitioners to rapidly compare the effectiveness of multiple interventions without needing long baseline phases. ATDs are typically evaluated using visual analysis and effect size analysis. However, past research suggests that different raters may come to different conclusions when analyzing these graphs. Furthermore, agreement between raters does not equate to accuracy. Artificial Intelligence (AI) technologies may improve the replicability of decision-making while performing adequately with smaller datasets. Within AI, Machine Learning (ML) algorithms can learn patterns and make data-driven predictions. These algorithms can quantify complex, intuitive patterns directly from data that would be difficult or impractical to define explicitly with traditional programming rules. This study investigates the use of ML technology to analyze ATD graphs. Specifically, the researchers examined which feature engineering techniques resulted in predictive performance for an ML model trained on simulated or non-simulated data and tested on non-simulated data. The best-performing models achieved classification accuracy above 90% with type 1 error rates below 15%. Deep neural networks (DNNs) led to the highest accuracy in detecting differentiated effects in ATD graphs while minimizing false positives. These results provide initial evidence that embedding DNNs into analysis of ATD single-case graphs could enhance the replicability of data analysis.