Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea.
Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
BMC Psychiatry. 2024 Nov 19;24(1):820. doi: 10.1186/s12888-024-06295-w.
We were interested in developing a methodology for diagnosing the depression status of a focused population group, such as the Korean university student group, with higher accuracy. To this end, we proposed a method of fusing the data collected from multiple depression self-questionnaires aided by a psychiatrist's diagnosis. In particular, we found that the standard diagnostic cut-offs and factor analysis prepared for a general population by depression self-questionnaires are inadequate for a focused population with its unique cultural background. In this study, a novel approach to optimizing diagnostic cut-offs and generalizing factor analysis for the Korean university student group is presented in the fusion space of multiple self-questionnaires.
We collected the data from 30 randomly selected Korean university students, over 21 weeks, with the psychiatric evaluation as a reference, then established the optimal cut-off regions in the fused CESD - PHQ9 score space based on the statistical correlation between CES - D and PHQ - 9 and the reference diagnostics. We also re-extracted the factors in the fused CESD - PHQ9 space to expose the key factors that are behind the depression characteristics of the group.
We verified the existence of a clear correlation between CES - D and PHQ - 9 scores. However, the standard cut-offs of CES - D and PHQ - 9 are found inconsistent with the correlation. The new cut-off regions we obtained in the fused CESD - PHQ9 score space are consistent with the correlation and optimal for the psychiatrist's diagnosis with the sensitivity and specificity of 80.95% and 89.74%, respectively. Also, we identified that "socio-psychological" and "interpersonal relationship" factors are the major factors for the depression characteristics of the group.
Although the new cut-off regions we presented were based on the incorporation of clinical diagnosis into the fused CESD - PHQ9 score space, further verification with a larger scale of clinical data is helpful.
We identified optimal cut-off regions and generalized factor analysis in the fusion space, which can provide more reliable and trustworthy diagnoses. These can serve as a self-diagnostic tool for reliably identifying the depression characteristics of a focused population as well as effectively linking individuals and psychiatrists as an intermediary.
我们有兴趣开发一种方法，以更高的准确性来诊断特定人群群体（如韩国大学生群体）的抑郁状态。为此，我们提出了一种融合由精神科医生诊断辅助的多个抑郁自评问卷收集的数据的方法。特别是，我们发现，为具有独特文化背景的特定人群准备的抑郁自评问卷的标准诊断截止值和因子分析不充分。在这项研究中，提出了一种在多个自评问卷的融合空间中优化诊断截止值和推广因子分析的新方法。
我们收集了 30 名随机选择的韩国大学生的数据，历时 21 周，以精神科评估作为参考，然后根据 CES-D 和 PHQ-9 与参考诊断之间的统计相关性，在融合的 CESD-PHQ9 评分空间中建立最佳的截止值区域。我们还重新提取了融合 CESD-PHQ9 空间中的因子，以揭示群体抑郁特征背后的关键因素。
我们验证了 CES-D 和 PHQ-9 评分之间存在明显的相关性。然而，标准的 CES-D 和 PHQ-9 截止值与相关性不一致。我们在融合的 CESD-PHQ9 评分空间中获得的新截止值区域与相关性一致，对精神科医生诊断的敏感性和特异性分别为 80.95%和 89.74%。此外，我们确定了“社会心理”和“人际关系”因素是群体抑郁特征的主要因素。
尽管我们提出的新截止值区域是基于将临床诊断纳入融合的 CESD-PHQ9 评分空间，但使用更大规模的临床数据进行进一步验证是有帮助的。
我们确定了融合空间中的最佳截止值区域和推广因子分析，这可以提供更可靠和值得信赖的诊断。这些可以作为可靠识别特定人群抑郁特征的自我诊断工具，以及有效地将个人与精神科医生联系起来作为中介。
