Subtle features in people’s everyday language may harbor the signs of future mental illness. Machine learning offers an approach for the rapid and accurate extraction of these signs. Here we investigate two potential linguistic indicators of psychosis in 40 participants of the North American Prodrome Longitudinal Study. We demonstrate how the linguistic marker of semantic density can be obtained using the mathematical method of vector unpacking, a technique that decomposes the meaning of a sentence into its core ideas. We also demonstrate how the latent semantic content of an individual’s speech can be extracted by contrasting it with the contents of conversations generated on social media, here 30,000 contributors to Reddit. The results revealed that conversion to psychosis is signaled by low semantic density and talk about voices and sounds. When combined, these two variables were able to predict the conversion with 93% accuracy in the training and 90% accuracy in the holdout datasets. The results point to a larger project in which automated analyses of language are used to forecast a broad range of mental disorders well in advance of their emergence.

人们日常语言中的微妙特征可能蕴含着未来精神疾病的征兆。机器学习为快速，准确地提取这些符号提供了一种方法。在这里，我们调查了北美前瞻性纵向研究的40名参与者中两种可能的精神病学语言指标。我们演示了如何使用向量解压缩的数学方法获得语义密度的语言标记，一种将句子的含义分解成其核心思想的技术。我们还演示了如何通过将语音的潜在语义内容与社交媒体上生成的对话的内容进行对比，来提取语音的潜在语义内容，此处有30,000位Reddit贡献者。结果表明，向精神病的转化是由于语义密度低，并谈论声音和声音而发出的。结合使用后，这两个变量能够以93％的训练精度和90％的保持数据集精度预测转换。结果指向了一个更大的项目，在该项目中，语言的自动分析被用于在各种精神障碍出现之前就对其进行预测。