Unité de Recherche Clinique Intersectorielle, Hôpital de Bohars, Centre Hospitalier Régional Universitaire de Brest, Bohars, France.
Institut Polytechnique de Paris, Palaiseau, France.
J Med Internet Res. 2022 Sep 6;24(9):e36986. doi: 10.2196/36986.
Schizophrenia is a disease associated with high burden, and improvement in care is necessary. Artificial intelligence (AI) has been used to diagnose several medical conditions as well as psychiatric disorders. However, this technology requires large amounts of data to be efficient. Social media data could be used to improve diagnostic capabilities.
The objective of our study is to analyze the current capabilities of AI to use social media data as a diagnostic tool for psychotic disorders.
A systematic review of the literature was conducted using several databases (PubMed, Embase, Cochrane, PsycInfo, and IEEE Xplore) using relevant keywords to search for articles published as of November 12, 2021. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria to identify, select, and critically assess the quality of the relevant studies while minimizing bias. We critically analyzed the methodology of the studies to detect any bias and presented the results.
Among the 93 studies identified, 7 studies were included for analyses. The included studies presented encouraging results. Social media data could be used in several ways to care for patients with schizophrenia, including the monitoring of patients after the first episode of psychosis. We identified several limitations in the included studies, mainly lack of access to clinical diagnostic data, small sample size, and heterogeneity in study quality. We recommend using state-of-the-art natural language processing neural networks, called language models, to model social media activity. Combined with the synthetic minority oversampling technique, language models can tackle the imbalanced data set limitation, which is a necessary constraint to train unbiased classifiers. Furthermore, language models can be easily adapted to the classification task with a procedure called "fine-tuning."
The use of social media data for the diagnosis of psychotic disorders is promising. However, most of the included studies had significant biases; we therefore could not draw conclusions about accuracy in clinical situations. Future studies need to use more accurate methodologies to obtain unbiased results.
精神分裂症是一种负担沉重的疾病，需要改善护理。人工智能 (AI) 已被用于诊断多种医疗状况和精神疾病。然而，这项技术需要大量数据才能有效运行。社交媒体数据可用于提高诊断能力。
我们的研究目的是分析人工智能利用社交媒体数据作为精神障碍诊断工具的当前能力。
使用几个数据库（PubMed、Embase、Cochrane、PsycInfo 和 IEEE Xplore）进行了系统文献回顾，使用相关关键字搜索截至 2021 年 11 月 12 日发表的文章。我们使用 PRISMA（系统评价和荟萃分析的首选报告项目）标准来识别、选择和批判性评估相关研究的质量，同时最大限度地减少偏差。我们批判性地分析了研究的方法学，以发现任何偏差，并呈现结果。
在确定的 93 项研究中，有 7 项研究被纳入分析。纳入的研究结果令人鼓舞。社交媒体数据可用于多种方式为精神分裂症患者提供护理，包括在精神病首次发作后对患者进行监测。我们在纳入的研究中发现了几个局限性，主要是缺乏获取临床诊断数据、样本量小以及研究质量的异质性。我们建议使用最先进的自然语言处理神经网络，称为语言模型，来模拟社交媒体活动。结合少数合成过采样技术，语言模型可以解决数据集不平衡的限制，这是训练无偏分类器的必要约束。此外，语言模型可以通过称为“微调”的过程轻松适应分类任务。
使用社交媒体数据诊断精神障碍具有广阔的前景。然而，纳入的大多数研究都存在显著的偏倚；因此，我们无法对临床情况下的准确性得出结论。未来的研究需要使用更准确的方法学来获得无偏的结果。
