机器学习发现预测心理“声音”字osis

它们的结果表明，两种语言变量的自动分析 - 更频繁地使用与声音和低语义密度的单词，或者用低语义密度，或模糊的方式使用 - 可以预测风险的人是否会以93％的准确性发展精神病。

Even trained clinicians had not noticed how people at risk for psychosis use more words associated with sound than the average, although abnormal auditory perception is a pre-clinical symptom. “Trying to hear these subtleties in conversations with people is like trying to see microscopic germs with your eyes,” says Neguine Rezaii, first author of the paper. “The automated technique we’ve developed is a really sensitive tool to detect these hidden patterns. It’s like a microscope for warning signs of psychosis.”

Rezaii began work on the paper while she was a resident at Emory School of Medicine’s Department of Psychiatry and Behavioral Sciences. She is now a fellow in Harvard Medical School’s Department of Neurology. “It was previously known that subtle features of future psychosis are present in people’s language, but we’ve used machine learning to actually uncover hidden details about those features,” says senior author Phillip Wolff, a professor of psychology at Emory. Wolff’s lab focuses on language semantics and machine learning to predict decision-making and mental health.

“Our finding is novel and adds to the evidence showing the potential for using machine learning to identify linguistic abnormalities associated with mental illness,” says co-author Elaine Walker, an Emory professor of psychology and neuroscience who researches how schizophrenia and other psychotic disorders develop.

The onset of schizophrenia and other psychotic disorders typically occurs in the early 20s, with warning signs — known as prodromal syndrome — beginning around age 17. About 25 to 30 percent of youth who meet criteria for a prodromal syndrome will develop schizophrenia or another psychotic disorder.

使用结构化访谈和认知测试，培训的临床医生可以预测精神病，在具有前驱综合征的人中的准确性约为80％。机器学习研究是简化诊断方法的许多持续努力，识别新变量，提高预测的准确性。

Currently, there is no cure for psychosis. “If we can identify individuals who are at risk earlier and use preventive interventions, we might be able to reverse the deficits,” Walker says. “There are good data showing that treatments like cognitive-behavioral therapy can delay onset, and perhaps even reduce the occurrence of psychosis.”

For the current paper, the researchers first used machine learning to establish “norms” for conversational language. They fed a computer software program the online conversations of 30,000 users of Reddit, a social media platform where people have informal discussions about a range of topics. The software program, known as Word2Vec, uses an algorithm to change individual words to vectors, assigning each one a location in a semantic space based on its meaning. Those with similar meanings are positioned closer together than those with far different meanings.

Wolff Lab还开发了一种计算机程序，以执行研究人员被称为“矢量解包”或分析单词使用的语义密度。以前的工作在句子之间测量了语义连贯性。矢量解包允许研究人员量化每个句子的信息。

在生成“正常”数据的基线之后，研究人员将与训练有素的临床医生进行的40名参与者的诊断访谈应用相同的技术，作为多网站北美前驱纵向研究（NAPLS）的一部分，由国家资助健康研究所。Napls专注于临床高风险的年轻人进行精神病。Walker是埃里斯的纳普尔人的主要调查员，其中九届项目九所大学之一。

The automated analyses of the participant samples were then compared to the normal baseline sample and the longitudinal data on whether the participants converted to psychosis. The results showed that higher than normal usage of words related to sound, combined with a higher rate of using words with similar meaning, meant that psychosis was likely on the horizon.

Strengths of the study include the simplicity of using just two variables — both of which have a strong theoretical foundation — the replication of the results in a holdout dataset, and the high accuracy of its predictions, at above 90 percent. “In the clinical realm, we often lack precision,” Rezaii says. “We need more quantified, objective ways to measure subtle variables, such as those hidden within language usage.”

Rezaii and Wolff are now gathering larger data sets and testing the application of their methods on a variety of neuropsychiatric diseases, including dementia. “This research is interesting not just for its potential to reveal more about mental illness, but for understanding how the mind works — how it puts ideas together,” Wolff says. “Machine learning technology is advancing so rapidly that it’s giving us tools to data mine the human mind.”