Fusion of multiple self-diagnostic questionnaires into optimal diagnostic cut-offs and factor analysis for depression...

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.