Bipolar Disorder and Oxidative Stress Injury Mechanism - Clinical Big Data Analysis Based on Machine Learning
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 3,702
- 试验地点
- 1
- 主要终点
- Early prediction model of bipolar disorder with oxidative stress index as the core
研究概览
简要总结
This study is a single-center, retrospective, cross-sectional study. We plan to work with our network information center to analysis the related indicators of oxidative stress injury in patients with bipolar disorder based on oxidative stress data. During the study, machine learning was used as a data analysis method to screen out the biomarker risk factors with sensitivity and specificity for early recognition of bipolar disorder from major depression disorder with oxidative stress injury as the core. And then build up effective clinical predictive models for early identification of bipolar disorder, which can predict the early quantitative probabilistic of the onset of bipolar disorder.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •age is not limited
- •gender is not limited
- •meets the diagnostic criteria for bipolar disorder of ICD-10 F31,F32 and its sub-categories
- •has relevant HIS system data that can be utilized.
排除标准
- •patients who did not meet the appeal diagnosis after three-level rounds of ward
- •patients who met the above three diagnoses but had severe data loss (missing value ≥ estimated data value of 30%)
结局指标
主要结局
Early prediction model of bipolar disorder with oxidative stress index as the core
时间窗: at August 2019
Based on the oxidative stress data, the study will analysis related indicators of oxidative stress injury in patients with bipolar disorder. Then use the method of machine learning to build up the early prediction model of bipolar disorder.
次要结局
未报告次要终点
研究者
Yiru FANG M.D., Ph.D.
director of clinical research center & division of mood disorders
Shanghai Mental Health Center