TY - JOUR AU - Le Glaz, Aziliz AU - Haralambous, Yannis AU - Kim-Dufor, Deok-Hee AU - Lenca, Philippe AU - Billot, Romain AU - Ryan, Taylor C AU - Marsh, Jonathan AU - DeVylder, Jordan AU - Walter, Michel AU - Berrouiguet, Sofian AU - Lemey, Christophe PY - 2021 DA - 201/5/4 TI -心理健康中的机器学习和自然语言处理:JO - J Med Internet Res SP - e15708 VL - 23 IS - 5kw -机器学习KW -自然语言处理KW -人工智能KW -数据挖掘KW -心理健康KW -精神病学AB -背景:机器学习系统是人工智能领域的一部分，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了良好的性能。目的:本系统综述的主要目的是总结和描述，在方法论和技术术语中，使用机器学习和NLP技术在心理健康方面的研究。次要目的是考虑这些方法在精神健康临床实践中的潜在用途。方法:本系统综述遵循PRISMA(系统综述和荟萃分析首选报告项目)指南，并在PROSPERO(系统综述前瞻性注册;CRD42019107376)。搜索使用4个医学数据库(PubMed、Scopus、ScienceDirect和PsycINFO)进行，搜索关键词为:机器学习、数据挖掘、精神病学、精神健康和精神障碍。排除标准如下:除英语以外的语言、匿名化过程、案例研究、会议论文和评论。对出版日期没有任何限制。结果:共识别出327篇文章，其中269篇(82.3%)被排除，58篇(17.7%)被纳入评审。 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. SN - 1438-8871 UR - //www.mybigtv.com/2021/5/e15708 UR - https://doi.org/10.2196/15708 UR - http://www.ncbi.nlm.nih.gov/pubmed/33944788 DO - 10.2196/15708 ID - info:doi/10.2196/15708 ER -