Abstract

Background

Digital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this purpose.

Aims

Machine learning was used to address this gap in the literature and determine whether: (1) diagnostic status could be classified from digital phenotyping measures relevant to negative symptoms and (2) the 5 negative symptom domains (anhedonia, avolition, asociality, alogia, and blunted affect) were differentially classified by active and passive digital phenotyping variables.

Methods

Participants included 52 outpatients with a PD and 55 healthy controls (CN) who completed 6 days of active (ecological momentary assessment surveys) and passive (geolocation, accelerometry) digital phenotyping data along with clinical ratings of negative symptoms.

Results

Machine learning algorithms classifying the presence of a PD diagnosis yielded 80% accuracy for cross-validation in H₂O AutoML and 79% test accuracy in the Recursive Feature Elimination with Cross Validation feature selection model. Models classifying the presence vs absence of clinically significant elevations on each of the 5 negative symptom domains ranged in test accuracy from 73% to 91%. A few active and passive features were highly predictive of all 5 negative symptom domains; however, there were also unique predictors for each domain.

Conclusions

These findings suggest that negative symptoms can be modeled from digital phenotyping data recorded in situ. Implications for selecting the most appropriate digital phenotyping variables for use as outcome measures in clinical trials targeting negative symptoms are discussed.

中文翻译：

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机器学习识别与精神障碍阴性症状最相关的数字表型测量：对临床试验的影响

 抽象的

 背景

数字表型分析已被提议作为一种新型评估工具，用于针对精神障碍 (PD) 阴性症状的临床试验。然而，尚不清楚哪种数字表型测量最适合此目的。

 目标

机器学习被用来弥补文献中的这一空白，并确定是否：(1) 诊断状态可以根据与阴性症状相关的数字表型测量进行分类，以及 (2) 5 个阴性症状领域（快感缺失、意志缺失、社交性缺失、失语症和失语症）钝化情感）通过主动和被动数字表型变量进行了不同的分类。

 方法

参与者包括 52 名患有 PD 的门诊患者和 55 名健康对照 (CN)，他们完成了 6 天的主动（生态瞬时评估调查）和被动（地理定位、加速测量）数字表型数据以及阴性症状的临床评级。

 结果

对 PD 诊断是否存在进行分类的机器学习算法在 H ₂ O AutoML 中的交叉验证准确率达到 80%，在带有交叉验证特征选择模型的递归特征消除中获得 79% 的测试准确率。对 5 个阴性症状领域中每一个领域是否存在临床显着升高进行分类的模型的测试准确度在 73% 到 91% 之间。一些主动和被动特征高度预测所有 5 个阴性症状领域；然而，每个领域也有独特的预测因子。

 结论

这些发现表明，阴性症状可以根据原位记录的数字表型数据进行建模。讨论了选择最合适的数字表型变量作为针对阴性症状的临床试验中的结果测量的含义。