Owusu Priscilla N, Reininghaus Ulrich, Koppe Georgia, Dankwa-Mullan Irene, Bärnighausen Till
Institute of Global Health, University Hospital Heidelberg, Heidelberg, Germany.
Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
PLoS One. 2021 Nov 8;16(11):e0259499. doi: 10.1371/journal.pone.0259499. eCollection 2021.
The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression.
We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies.
We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media.
International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).
社交媒体的普及导致了围绕心理健康状况，尤其是抑郁症的用户群体的聚集。社交媒体为情境化和预测用户自我报告的抑郁负担提供了丰富的环境。现代人工智能（AI）方法常用于分析社交媒体上用户生成的情绪。在即将进行的系统评价中，我们将根据标准诊断框架检验这些基于计算机的健康监测模型的内容效度。从临床角度出发，我们将尝试对这些现代人工智能应用在抑郁症检测方面的优势做出规范性判断。
我们将对2010年至2020年发表在PubMed、美国心理学会心理学文摘数据库（APA PsychInfo）、科学Direct、EMBASE心理学数据库（EMBASE Psych）、谷歌学术和科学网（Web of Science）上的英文和德文出版物进行系统评价。纳入标准包括队列研究、病例对照研究、横断面研究、随机对照研究以及会议论文报告。系统评价将排除一些灰色来源材料，特别是社论、报纸文章和博客文章。我们的主要结局是社交媒体上自我报告的抑郁症。次要结局将是用于社交媒体抑郁症筛查的人工智能方法类型以及伴随这些方法的临床验证程序。第二步，我们将利用强化证据的人群、干预措施、对照、结局、研究类型（PICOS）工具来完善我们的纳入和排除标准。在两位作者对证据来源的偏倚风险进行独立评估之后，数据提取过程将最终形成对所审查研究的主题综合。
我们提出了一项系统评价的方案，该方案将考虑来自同行评审出版物来源的所有与主要和次要结局相关的现有文献。完成的评价将把抑郁症作为社交媒体材料中自我报告的健康结局进行讨论。我们将研究常用于在线抑郁症监测的计算方法，包括人工智能和机器学习技术。此外，我们将在算法设计中关注作为内容效度指标的标准临床评估。算法临床结构的方法学质量将使用基于共识的健康状况测量工具选择标准（COSMIN）框架进行评估。我们通过对当前人工智能在社交媒体上筛查抑郁症应用的规范性判断来结束本研究。
国际前瞻性系统评价注册库PROSPERO（注册号CRD42020187874）
