Hu Yunyun, Chen Jiang, Li Jian, Xu Zhi
Key Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, 210096, Nanjing, China.
Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing, 210009, China.
Heliyon. 2024 Jul 4;10(14):e33973. doi: 10.1016/j.heliyon.2024.e33973. eCollection 2024 Jul 30.
Major depression is a complex psychiatric disorder that includes genetic, neurological, and cognitive factors. Early detection and intervention can prevent progression, and help select the best treatment. Traditional clinical diagnosis tends to be subjective and misdiagnosed. Based on this, this study leverages clinical scale assessments and sequencing data to construct disease prediction models. Firstly, data undergoes preprocessing involving normalization and other requisite procedures. Feature engineering is then applied to curate subsets of features, culminating in the construction of a model through the implementation of machine learning and deep learning algorithms. In this study, 18 features with significant differences between patients and healthy controls were selected. The depression recognition model was constructed by deep learning with an accuracy of 87.26 % and an AUC of 91.56 %, which can effectively distinguish patients with depression from healthy controls. In addition, 33 features selected by recursive feature elimination method were used to construct a prognostic effect model of patients after 2 weeks of treatment, with an accuracy of 75.94 % and an AUC of 83.33 %. The results show that the deep learning algorithm based on clinical and sequencing data has good accuracy and provides an objective and accurate method for the diagnosis and pharmacodynamic prediction of depression. Furthermore, the selected differential features can serve as candidate biomarkers to provide valuable clues for diagnosis and efficacy prediction.
重度抑郁症是一种复杂的精神疾病，涉及遗传、神经和认知因素。早期发现和干预可以预防病情进展，并有助于选择最佳治疗方法。传统的临床诊断往往具有主观性且容易误诊。基于此，本研究利用临床量表评估和测序数据构建疾病预测模型。首先，数据要经过包括归一化等必要程序的预处理。然后应用特征工程来精心挑选特征子集，最终通过实施机器学习和深度学习算法构建模型。在本研究中，选择了患者与健康对照之间存在显著差异的18个特征。通过深度学习构建的抑郁症识别模型，准确率为87.26%，曲线下面积（AUC）为91.56%，能够有效地区分抑郁症患者与健康对照。此外，使用通过递归特征消除方法选择的33个特征构建了治疗2周后患者的预后效果模型，准确率为75.94%，AUC为83.33%。结果表明，基于临床和测序数据的深度学习算法具有良好的准确性，为抑郁症的诊断和药效预测提供了一种客观准确的方法。此外，所选的差异特征可作为候选生物标志物，为诊断和疗效预测提供有价值的线索。
