Bipolar Disorder and Oxidative Stress Injury Mechanism - Clinical Big Data Analysis Based on Machine Learning

Shanghai Mental Health Center3,702 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

3,702

Conditions

Bipolar Disorder

Eligibility

Sex: ALL

Medical Language ↔ Plain English

Inclusion Criteria: * 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. Exclusion Criteria: * 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%)

Locations (1)

Shanghai Mental Health Center

Shanghai, Shanghai Municipality, China

Outcomes

Primary Outcomes

Early prediction model of bipolar disorder with oxidative stress index as the core

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.

Time frame: at August 2019