Background: New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps. Objective: This study aimed to reveal the profiles of users of a mental health app through machine learning techniques. Methods: We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login. Results: The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features. Conclusions: User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.

中文翻译：

---

使用数据挖掘技术评估的eHealth用户的精神病学特征：队列研究

背景：新技术正在改变对医疗记录的访问以及医生与患者之间的关系。专业人士现在可以使用电子心理健康工具为精神疾病患者提供及时和个性化的响应。但是，缺乏有关使用电子心理健康应用程序的患者的数字表型的知识。目的：本研究旨在通过机器学习技术揭示心理健康应用程序用户的概况。方法：我们应用了非参数模型，即稀疏泊松分解模型，以发现2254名精神科门诊患者对总体健康状况进行简短自我评估时反应模式的潜在特征。首次登录后，通过心理健康应用程序完成了评估。结果：结果显示以下四个不同的患者特征：（1）所有患者都有不值钱，攻击性和自杀观念的感觉；（2）四分之一的人表示精力不足，难以解决问题；（3）少于四分之一的描述性抑郁症状具有极高的自杀念头和攻击性；（4）少数，可能是最恶劣的条件，报告了所有这些特征的组合。结论：用户资料与临床医生做出的诊断没有重叠。由于每个配置文件似乎都与不同级别的严重性相关联，因此这些配置文件可用于预测电子心理健康应用程序用户之间的行为风险。