Background: Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. Objective: The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice Methods: This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. Results: A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Conclusions: Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

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中文翻译：

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心理健康中的机器学习和自然语言处理：系统回顾


背景：机器学习系统是人工智能领域的一部分，它自动从数据中学习模型以做出更好的决策。自然语言处理（NLP）通过使用语料库和学习方法，在文本分类或情感挖掘等统计任务中提供良好的性能。目的：本系统综述的主要目的是从方法论和技术角度总结和描述使用机器学习和 NLP 技术进行心理健康的研究。第二个目的是考虑这些方法在心理健康临床实践中的潜在用途。方法：本系统评价遵循 PRISMA（系统评价和荟萃分析的首选报告项目）指南，并在 PROSPERO（系统评价前瞻性登记册）注册；编号 CRD42019107376）。该搜索是使用 4 个医学数据库（PubMed、Scopus、ScienceDirect 和 PsycINFO）进行的，关键词如下：机器学习、数据挖掘、精神病学、心理健康和精神障碍。排除标准如下：英语以外的语言、匿名过程、案例研究、会议论文和评论。对发布日期没有施加任何限制。结果：共鉴定出327篇文章，其中269篇（82.3%）被排除，58篇（17.7%）纳入综述。结果是通过定性角度组织的。尽管研究的主题和方法各不相同，但还是出现了一些主题。人群研究可以分为三类：医疗数据库中的患者、来到急诊室的患者以及社交媒体用户。 主要目标是提取症状、对疾病严重程度进行分类、比较治疗效果、提供精神病理学线索并挑战当前的疾病诊断学。医疗记录和社交媒体是两个主要数据源。在使用的方法上，预处理使用了自然语言处理的标准方法和专用于医学文本的唯一标识符提取。高效的分类器比透明功能的分类器更受青睐。 Python 是最常用的平台。结论：机器学习和自然语言处理模型近年来一直是医学界的热门话题，可能被认为是医学研究的新范式。然而，这些过程倾向于证实临床假设，而不是开发全新的信息，并且只有一个主要类别的人群（即社交媒体用户）是不精确的群体。此外，一些特定于语言的功能可以提高 NLP 方法的性能，并且应该更仔细地研究它们对其他语言的扩展。然而，机器学习和 NLP 技术可以从未经探索的数据（即护理人员通常无法获取的患者的日常习惯）中提供有用的信息。在将其视为精神卫生保健的附加工具之前，伦理问题仍然存在，应及时讨论。机器学习和 NLP 方法可以为心理健康研究提供多种视角，但也应被视为支持临床实践的工具。


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