Applied Artificial Intelligence Lab, Computer Science Department, School of Science, Buenos Aires University, CONICET, Buenos Aires 1428, Argentina; CONICET-Universidad de Buenos Aires, Instituto de Investigación en Ciencias de la Computación (ICC), Buenos Aires, Argentina.
Integrative Neuroscience Lab, Universidad Torcuato Di Tella, CONICET, Buenos Aires 1428, Argentina.
J Affect Disord. 2018 Apr 1;230:84-86. doi: 10.1016/j.jad.2018.01.006.
Natural speech analytics has seen some improvements over recent years, and this has opened a window for objective and quantitative diagnosis in psychiatry. Here, we used a machine learning algorithm applied to natural speech to ask whether language properties measured before psilocybin for treatment-resistant can predict for which patients it will be effective and for which it will not.
A baseline autobiographical memory interview was conducted and transcribed. Patients with treatment-resistant depression received 2 doses of psilocybin, 10 mg and 25 mg, 7 days apart. Psychological support was provided before, during and after all dosing sessions. Quantitative speech measures were applied to the interview data from 17 patients and 18 untreated age-matched healthy control subjects. A machine learning algorithm was used to classify between controls and patients and predict treatment response.
Speech analytics and machine learning successfully differentiated depressed patients from healthy controls and identified treatment responders from non-responders with a significant level of 85% of accuracy (75% precision).
Automatic natural language analysis was used to predict effective response to treatment with psilocybin, suggesting that these tools offer a highly cost-effective facility for screening individuals for treatment suitability and sensitivity.
The sample size was small and replication is required to strengthen inferences on these results.
近年来，自然语言分析取得了一些进展，为精神病学的客观和定量诊断开辟了一扇窗。在这里，我们使用一种应用于自然语言的机器学习算法，询问在使用 psilocybin 治疗之前测量的语言特性是否可以预测哪些患者将有效，哪些患者无效。
进行基线自传体记忆访谈并进行转录。治疗抵抗性抑郁症患者接受了 2 剂 psilocybin，剂量分别为 10mg 和 25mg，间隔 7 天。在所有给药阶段之前、期间和之后都提供心理支持。将定量言语测量应用于 17 名患者和 18 名未经治疗的年龄匹配健康对照者的访谈数据。使用机器学习算法对控制组和患者进行分类，并预测治疗反应。
言语分析和机器学习成功地区分了抑郁患者和健康对照组，并以 85%的准确率（75%的精度）识别出治疗反应者和非反应者。
自动自然语言分析被用于预测对 psilocybin 治疗的有效反应，这表明这些工具为筛选适合治疗和敏感性的个体提供了一种极具成本效益的设施。
样本量较小，需要复制这些结果以加强推论。
