Detection of mental disorders from textual input is an emerging field for applied machine and deep learning methods. Here, we explore the limits of automated detection of autism spectrum disorder (ASD) and schizophrenia (SCZ). We compared the performance of: (1) dedicated diagnostic tools that involve collecting textual data, (2) automated methods applied to the data gathered by these tools, and (3) psychiatrists. Our article tests the effectiveness of several baseline approaches, such as bag of words and dictionary-based vectors, followed by a machine learning model. We employed two more refined Sentic text representations using affective features and concept-level analysis on texts. Further, we applied selected state-of-the-art deep learning methods for text representation and inference, as well as experimented with transfer and zero-shot learning. Finally, we also explored few-shot methods dedicated to low data size scenarios, which is a typical problem for the clinical setting. The best breed of automated methods outperformed human raters (psychiatrists). Cross-dataset approaches turned out to be useful (only from SCZ to ASD) despite different data types. The few-shot learning methods revealed promising results on the SCZ dataset. However, more effort is needed to explore the approaches to efficiently training models, given the very limited amounts of labeled clinical data. Psychiatry is one of the few medical fields in which the diagnosis of most disorders is based on the subjective assessment of a psychiatrist. Therefore, the introduction of objective tools supporting diagnostics seems to be pivotal. This paper is a step in this direction.

从文本输入中检测精神障碍是应用机器和深度学习方法的新兴领域。在这里，我们探讨了自闭症谱系障碍（ASD）和精神分裂症（SCZ）自动检测的局限性。我们比较了以下各项的性能：（1）涉及收集文本数据的专用诊断工具；（2）应用于由这些工具收集的数据的自动化方法；以及（3）精神科医生。我们的文章测试了几种基线方法的有效性，例如单词袋和基于字典的向量，然后是机器学习模型。我们使用情感特征和文本的概念级分析，采用了两种更精致的Sentic文本表示形式。此外，我们将选定的最先进的深度学习方法应用于文本表示和推理，并尝试了转移和零击学习。最后，我们还探索了针对低数据量场景的少量方法，这是临床环境中的典型问题。最好的自动方法种类胜过人类评价者（精神科医生）。尽管数据类型不同，但跨数据集方法仍然有用（仅从SCZ到ASD）。少量的学习方法在SCZ数据集上显示了可喜的结果。但是，鉴于标记的临床数据数量非常有限，需要付出更多的努力来探索有效训练模型的方法。精神病学是基于精神病医生的主观评估来诊断大多数疾病的少数医学领域之一。因此，引入支持诊断的客观工具似乎至关重要。