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

Study Overview
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Brief Summary
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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.

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

Conditions
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Bipolar Disorder

Participation Criteria
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Eligibility Criteria
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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%)
Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| bipolar disorder(BD)<br>hospitalized patients BD patients in SMHC from 2009 to 2018; meet ICD-10 diagnosis of F31 bipolar disorder criteria and its subtype (mental examination was conducted by three levels of doctors including at least one attending physician and one chief physician in psychiatric); available relevant HIS system biochemical data; hospitalized patients need to the first admission; age and gender is not limited | |
| major depressive disorder（MDD）<br>hospitalized patients MDD patients in SMHC from 2009 to 2018; meet ICD-10 diagnosis of F32 depressive disorder criteria and its subtype (mental examination was conducted by three levels of doctors including at least one attending physician and one chief physician in psychiatric); available relevant HIS system biochemical data; hospitalized patients need to the first admission; age and gender is not limited | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | at August 2019 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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bipolar disorder, machine learning