Bipolar Disorder
Conditions
Keywords
bipolar disorder, machine learning
Brief summary
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.
Interventions
None listed
Sponsors
Shanghai Mental Health Center
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
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%)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Early prediction model of bipolar disorder with oxidative stress index as the core | 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. |
Countries
China
Outcome results
None listed