Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression
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DOI：
10.1016/j.jad.2018.01.006
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发表时间：
2018-04-01
影响因子：
6.6
通讯作者：
Carhart-Harris, Robin L.
中科院分区：
文献类型：
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作者：
Carrillo, Facundo;Sigman, Mariano;Carhart-Harris, Robin L.
背景：自然语音分析近年来取得了一些进步，这为精神病学的客观和定量诊断打开了一扇窗。在这里，我们使用了一种应用于自然语音的机器学习算法，以询问在裸盖菇素治疗抗药性之前测量的语言特性是否可以预测它对哪些患者有效，哪些患者无效。方法：进行基线自传体记忆访谈并记录。难治性抑郁症患者接受2剂裸盖菇素，10 mg和25 mg，间隔7天。在所有给药之前、期间和之后都提供了心理支持。对17名患者和18名未接受治疗的年龄匹配健康对照者的访谈数据进行定量言语测量。使用机器学习算法对对照组和患者进行分类，并预测治疗反应。结果：语音分析和机器学习成功地将抑郁症患者与健康对照区分开来，并以85%的准确率（75%的准确率）显著水平识别治疗反应者和无反应者。结论：自动自然语言分析用于预测裸盖菇素治疗的有效反应，表明这些工具为筛选个体的治疗适用性和敏感性提供了一种高成本效益的设施。局限性：样本量较小，需要重复研究来加强对这些结果的推断。
Background: 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.Methods: 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.Results: 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).Conclusions: 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.Limitations: The sample size was small and replication is required to strengthen inferences on these results.