Background: Health researchers are increasingly using natural language processing (NLP) to study various mental health conditions using both social media and electronic health records (EHRs). There is currently no published synthesis that relates specifically to the use of NLP methods for bipolar disorder, and this scoping review was conducted to synthesize valuable insights that have been presented in the literature. Objective: This scoping review explored how NLP methods have been used in research to better understand bipolar disorder and identify opportunities for further use of these methods. Methods: A systematic, computerized search of index and free-text terms related to bipolar disorder and NLP was conducted using 5 databases and 1 anthology: MEDLINE, PsycINFO, Academic Search Ultimate, Scopus, Web of Science Core Collection, and the ACL Anthology. Results: Of 507 identified studies, a total of 35 (6.9%) studies met the inclusion criteria. A narrative synthesis was used to describe the data, and the studies were grouped into four objectives: prediction and classification (n=25), characterization of the language of bipolar disorder (n=13), use of EHRs to measure health outcomes (n=3), and use of EHRs for phenotyping (n=2). Ethical considerations were reported in 60% (21/35) of the studies. Conclusions: The current literature demonstrates how language analysis can be used to assist in and improve the provision of care for people living with bipolar disorder. Individuals with bipolar disorder and the medical community could benefit from research that uses NLP to investigate risk-taking, web-based services, social and occupational functioning, and the representation of gender in bipolar disorder populations on the web. Future research that implements NLP methods to study bipolar disorder should be governed by ethical principles, and any decisions regarding the collection and sharing of data sets should ultimately be made on a case-by-case basis, considering the risk to the data participants and whether their privacy can be ensured.

中文翻译：

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自然语言处理方法和双相情感障碍：范围审查

背景：健康研究人员越来越多地使用自然语言处理 (NLP) 通过社交媒体和电子健康记录 (EHR) 研究各种心理健康状况。目前还没有发表的综合文章专门涉及使用 NLP 方法治疗双相情感障碍，并且进行此范围审查是为了综合文献中提出的有价值的见解。目的：本范围审查探讨了如何在研究中使用 NLP 方法来更好地了解双相情感障碍并确定进一步使用这些方法的机会。方法：使用 5 个数据库和 1 个选集对双相情感障碍和 NLP 相关的索引和自由文本术语进行了系统的计算机化搜索：MEDLINE、PsycINFO、Academic Search Ultimate、Scopus、Web of Science Core Collection 和 ACL Anthology。结果：在 507 项确定的研究中，共有 35 项 (6.9%) 研究符合纳入标准。叙述性综合用于描述数据，研究分为四个目标：预测和分类（n=25）、双相情感障碍语言的表征（n=13）、使用 EHR 测量健康结果（n =3)，以及使用 EHR 进行表型分析 (n=2)。60% (21/35) 的研究报告了伦理考虑。结论：目前的文献展示了如何使用语言分析来帮助和改善对双相情感障碍患者的护理。双相情感障碍患者和医学界可以从使用 NLP 来调查风险承担、基于网络的服务、社会和职业功能以及网络上双相情感障碍人群的性别代表性的研究中受益。未来采用 NLP 方法研究双相情感障碍的研究应遵循伦理原则，关于数据集的收集和共享的任何决定最终都应根据具体情况做出，考虑到数据参与者的风险以及是否他们的隐私可以得到保证。