Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has the potential to improve outcomes for patients with ASD. It can be delivered at different intensities, which are classified as comprehensive or focused treatment approaches. Comprehensive ABA targets multiple developmental domains and involves 20–40 h/week of treatment. Focused ABA targets individual behaviors and typically involves 10–20 h/week of treatment. Determining the appropriate treatment intensity involves patient assessment by trained therapists, however, the final determination is highly subjective and lacks a standardized approach. In our study, we examined the ability of a machine learning (ML) prediction model to classify which treatment intensity would be most suited individually for patients with ASD who are undergoing ABA treatment. Retrospective data from 359 patients diagnosed with ASD were analyzed and included in the training and testing of an ML model for predicting comprehensive or focused treatment for individuals undergoing ABA treatment. Data inputs included demographics, schooling, behavior, skills, and patient goals. A gradient-boosted tree ensemble method, XGBoost, was used to develop the prediction model, which was then compared against a standard of care comparator encompassing features specified by the Behavior Analyst Certification Board treatment guidelines. Prediction model performance was assessed via area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The prediction model achieved excellent performance for classifying patients in the comprehensive versus focused treatment groups (AUROC: 0.895; 95% CI 0.811–0.962) and outperformed the standard of care comparator (AUROC 0.767; 95% CI 0.629–0.891). The prediction model also achieved sensitivity of 0.789, specificity of 0.808, PPV of 0.6, and NPV of 0.913. Out of 71 patients whose data were employed to test the prediction model, only 14 misclassifications occurred. A majority of misclassifications (n = 10) indicated comprehensive ABA treatment for patients that had focused ABA treatment as the ground truth, therefore still providing a therapeutic benefit. The three most important features contributing to the model’s predictions were bathing ability, age, and hours per week of past ABA treatment. This research demonstrates that the ML prediction model performs well to classify appropriate ABA treatment plan intensity using readily available patient data. This may aid with standardizing the process for determining appropriate ABA treatments, which can facilitate initiation of the most appropriate treatment intensity for patients with ASD and improve resource allocation.

中文翻译：

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应用行为分析治疗计划类型的机器学习确定

应用行为分析 (ABA) 被认为是治疗自闭症谱系障碍 (ASD) 的金标准，并有可能改善 ASD 患者的预后。它可以以不同的强度进行，分为综合治疗方法或重点治疗方法。综合 ABA 针对多个发育领域，涉及 20-40 小时/周的治疗。重点 ABA 针对个人行为，通常每周治疗 10-20 小时。确定合适的治疗强度需要由受过训练的治疗师对患者进行评估，但是，最终的决定是高度主观的，并且缺乏标准化的方法。在我们的研究中，我们检查了机器学习 (ML) 预测模型的能力，以对哪种治疗强度最适合正在接受 ABA 治疗的 ASD 患者进行分类。对 359 名诊断为 ASD 患者的回顾性数据进行了分析，并将其纳入 ML 模型的训练和测试中，以预测接受 ABA 治疗的个体的综合治疗或重点治疗。数据输入包括人口统计、教育、行为、技能和患者目标。梯度提升树集成方法 XGBoost 用于开发预测模型，然后将其与包含行为分析师认证委员会治疗指南规定的特征的护理标准进行比较。预测模型性能通过接受者操作特征曲线下的面积 (AUROC) 进行评估，敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。该预测模型在对综合治疗组与重点治疗组患者进行分类方面取得了出色的性能（AUROC：0.895；95% CI 0.811–0.962），并且优于护理标准（AUROC 0.767；95% CI 0.629–0.891）。该预测模型还实现了 0.789 的灵敏度、0.808 的特异性、0.6 的 PPV 和 0.913 的 NPV。在其数据用于测试预测模型的 71 名患者中，只有 14 名患者发生了错误分类。大多数错误分类 (n = 10) 表明对以 ABA 治疗为基本事实的患者进行综合 ABA 治疗，因此仍然提供治疗益处。有助于模型预测的三个最重要的特征是沐浴能力、年龄、过去 ABA 治疗的每周小时数。这项研究表明，ML 预测模型在使用现成的患者数据对适当的 ABA 治疗计划强度进行分类方面表现良好。这可能有助于标准化确定适当的 ABA 治疗的过程，这可以促进为 ASD 患者启动最合适的治疗强度并改善资源分配。