基于机器学习策略的乙型肝炎患者抑郁风险预测模型的构建
Siyi Wang1, Haoqi Liu1, Chen Liang2
1The Second Clinical Medical College, Beijing University of Traditional Chinese Medicine, Beijing, China.
PloS one
|February 4, 2026
概括
机器学习模型现在可以预测乙型肝炎患者的抑郁风险. 关键因素包括肝功能,炎症和社会经济地位,使得更好的心理健康管理.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 乙型肝炎 (HBV) 感染带来了重大的心理健康风险,包括抑郁症.
- 目前对HBV患者的抑郁查方法缺乏客观的预测工具.
- 这项研究解决了对HBV患者有效抑郁风险评估的需求.
研究的目的:
- 开发一种基于机器学习的风险预测模型,用于乙型肝炎患者的抑郁症.
- 确定影响该人口抑郁风险的关键特征.
- 加强乙型肝炎患者的心理健康管理策略.
主要方法:
- 使用NHANES数据库获取人口,饮食,身体,实验室和问卷数据.
- 应用随机森林用于特征选和五种机器学习模型 (MLPClassifier,梯度提升,后勤回归,AdaBoost,LDA) 进行预测.
- 使用AUC,准确性,回忆,精度,F1得分,ROC曲线,校准曲线和DCA评估模型性能.
主要成果:
- 所有五种模型都在HBV患者中表现出强大的抑郁风险预测性能.
- MLPClassifier获得了最高的性能 (AUC:0.935,回忆:0.980,F1得分:0.917).
- 有意义的预测因素包括肝功能 (bilirubin,性酸酶),电解质平衡 (),炎症 (RDW,淋巴细胞) 和社会经济因素 (贫困收入比例,种族).
结论:
- 开发了一种高效客观的机器学习模型,用于预测乙型肝炎患者的抑郁风险.
- 该模型为准确的查和个性化的心理健康管理提供了一个新的工具.
- 这些发现突出了与HBV相关的抑郁症中生理,生化和社会经济因素的相互作用,为未来的干预提供了信息.
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