Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; Department of Medical Sciences, Graduate School of Ajou University, Suwon, South Korea.
Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea.
Psychiatry Res. 2024 Apr;334:115817. doi: 10.1016/j.psychres.2024.115817. Epub 2024 Feb 25.
Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.
尽管接受治疗的抑郁症患者中有 20% 无法达到缓解，但预测治疗抵抗性抑郁症（TRD）仍然具有挑战性。在这项研究中，我们旨在使用结构化电子病历数据、脑形态计量学和自然语言处理开发一种可解释的多模态 TRD 预测模型。共有 247 名新发抑郁发作患者入组。TRD 预测模型基于以下参数的组合而建立：选择的表格数据集特征、脑 T1 加权磁共振成像（MRI）的独立成分图权重以及临床记录中的主题概率。所有模型均通过五折交叉验证应用极端梯度提升（XGBoost）算法。使用所有数据源的模型显示出最高的接收器操作特征曲线下面积为 0.794，其次是同时使用脑 MRI 和结构化数据、脑 MRI 和临床记录、临床记录和结构化数据、脑 MRI 仅、结构化数据仅和临床记录仅的模型（分别为 0.770、0.762、0.728、0.703、0.684 和 0.569）。TRD 的分类由几个预测因子驱动，例如先前接触抗抑郁药和抗高血压药物、感觉运动网络、默认模式网络和躯体症状。我们的研究结果表明，将临床数据与神经影像学和自然语言处理变量相结合可以提高 TRD 的预测能力。
