Qiu Jianqing, Zhu Ting, Qin Ke, Zhang Wei
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Medical Big Data Center, Sichuan University, Chengdu, China.
BMC Psychiatry. 2025 Jan 28;25(1):81. doi: 10.1186/s12888-025-06510-2.
The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness. Numerous studies have sought to use RDoC to understand the Diagnostic and Statistical Manual of Mental Disorders (DSM) categories from a qualified perspective, but few studies have examined the distribution variations and interaction characteristics of RDoC within various DSM categories through retrospective analyses. Therefore, we employed unsupervised learning to translate five domains of eRDoC scores derived from electronic medical records (EMR) of patients diagnosed with Major Depressive Disorder (MDD), Schizophrenia (SCZ), and Bipolar Disorder (BD) at West China Hospital between 2008 and 2021. The distribution characteristics, interaction networks, and potential clinical effectiveness of RDoC domains were analyzed. Using non-parametric statistical tests, we found that MDD had the highest score in Negative Valence System (NVS) (4.1, p < 0.001), while BD exhibited the highest score in Positive Valence System (PVS) score (4.9, p < 0.001) and Arousal System (AS) (4.4, p < 0.001). SCZ demonstrated the highest scores in Cognitive Systems (CS) (5.8, p < 0.001) and Social Processes Systems (SPS) (4.6, p < 0.001). Through Bayesian network (BN) analysis, we identified relatively consistent interaction relationships among various RDoC domains (NVS → AS, NVS → CS, NVS → PVS, as well as CS → SPS; parameter range = 0.156 to 0.635, p < 0.001). Lastly, using logistic regression and Cox proportional hazards models, we demonstrated that AS was significantly associated with the length of hospital stay (-0.21, p < 0.05) and 30-day readmission risk (adjusted odds ratio [aOR] = 0.91, 95% confidence interval [CI] 0.91-0.99) to some extent. In conclusion, we suggest that the eRDoC characteristics varied in different DSM. By Bayesian Network, we found NVS and CS might be potential source in interacting with other system. Furthermore, CS, SPS and AS were associated with the length of stay and 30-days readmission, making them effective for predicting prognosis of psychiatric disorders.
当前以《精神疾病诊断与统计手册》（DSM）为导向的诊断范式引入了异质性问题，因为它未能考虑到对精神疾病潜在神经学过程的识别，这影响了治疗的精确性。研究领域标准（RDoC）框架是解决这种异质性的一种公认方法，并且已经提出了几种评估和转化技术。在这些方法中，使用自然语言处理（NLP）从电子病历（EMR）中转换RDoC分数已成为一种合适的技术，并显示出临床有效性。许多研究试图从合格的角度使用RDoC来理解《精神疾病诊断与统计手册》（DSM）类别，但很少有研究通过回顾性分析来检查RDoC在各种DSM类别中的分布差异和相互作用特征。因此，我们采用无监督学习方法，对2008年至2021年期间在华西医院被诊断为重度抑郁症（MDD）、精神分裂症（SCZ）和双相情感障碍（BD）的患者的电子病历（EMR）中得出的电子RDoC分数的五个领域进行转换。分析了RDoC领域的分布特征、相互作用网络和潜在的临床有效性。使用非参数统计检验，我们发现MDD在负性效价系统（NVS）中得分最高（4.1，p < 0.001），而BD在正性效价系统（PVS）得分（4.9，p < 0.001）和唤醒系统（AS）（4.4，p < 0.001）中得分最高。SCZ在认知系统（CS）（5.8，p < 0.001）和社会过程系统（SPS）（4.6，p < 0.001）中得分最高。通过贝叶斯网络（BN）分析，我们确定了各个RDoC领域之间相对一致的相互作用关系（NVS→AS、NVS→CS、NVS→PVS，以及CS→SPS；参数范围=0.156至0.635，p < 0.001）。最后，使用逻辑回归和Cox比例风险模型，我们证明AS在一定程度上与住院时间长度（-0.21，p < 0.05）和30天再入院风险（调整后的优势比[aOR]=0.91，95%置信区间[CI]0.91 - 0.99）显著相关。总之，我们认为不同DSM中的电子RDoC特征存在差异。通过贝叶斯网络，我们发现NVS和CS可能是与其他系统相互作用的潜在源头。此外，CS、SPS和AS与住院时间长度和30天再入院相关，使其对预测精神疾病的预后有效。
