Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose rising prevalence places increasing demands on a lengthy diagnostic process. Machine learning (ML) has shown promise in automating ASD diagnosis, but most existing models operate as black boxes and are typically trained on a single dataset, limiting their generalizability. In this study, we introduce a transparent and interpretable ML approach that leverages BioBERT, a state-of-the-art language model, to analyze unstructured clinical text. The model is trained to label descriptions of behaviors and map them to diagnostic criteria, which are then used to assign a final label (ASD or not). We evaluate transfer learning, the ability to transfer knowledge to new data, using two distinct real-world datasets. We trained on datasets sequentially and mixed together and compared the performance of the best models and their ability to transfer to new data. We also created a black-box approach and repeated this transfer process for comparison. Our transparent model demonstrated robust performance, with the mixed-data training strategy yielding the best results (97 % sensitivity, 98 % specificity). Sequential training across datasets led to a slight drop in performance, highlighting the importance of training data order. The black-box model performed worse (90 % sensitivity, 96 % specificity) when trained sequentially or with mixed data. Overall, our transparent approach outperformed the black-box approach. Mixing datasets during training resulted in slightly better performance and should be the preferred approach when practically possible. This work paves the way for more trustworthy, generalizable, and clinically actionable AI tools in neurodevelopmental diagnostics.
Abstract (translated)
自闭症谱系障碍（ASD）是一种复杂的神经发育状况，其发病率的上升给诊断流程带来了更大的压力。机器学习（ML）在自动化ASD诊断方面显示出潜力，但大多数现有模型都属于黑箱操作，并且通常仅在一个数据集上进行训练，这限制了它们的泛化能力。在这项研究中，我们介绍了一种透明且可解释的机器学习方法，该方法利用BioBERT这一最先进的语言模型来分析非结构化的临床文本。通过训练模型将行为描述与诊断标准相匹配，并以此为基础确定最终标签（ASD或否），我们的模型能够进行此操作。 为了评估知识转移的能力，即向新数据迁移知识的能力，我们使用了两个不同的现实世界数据集进行了测试。我们顺序地和混合训练数据集来训练这些模型，并比较最佳模型在将知识转移到新数据时的性能。此外，我们还创建了一个黑箱方法并重复了这一过程以进行对比。 我们的透明模型展示了稳健的表现，在混合数据训练策略下表现最好（97% 的灵敏度、98% 的特异性）。跨数据集顺序训练导致性能略有下降，这突显了训练数据顺序的重要性。相比之下，黑箱模型在顺序训练或混合训练数据时表现较差（90% 的灵敏度、96% 的特异性）。 总体而言，我们的透明方法优于黑盒方法。在培训期间混合数据可以略微提高性能，在实践中可行的情况下应优先采用这种方法。这项工作为神经发育诊断领域开发出更多值得信赖的、具有泛化能力的和临床适用的人工智能工具铺平了道路。
URL
https://arxiv.org/abs/2512.06161
