Detection Study of Bipolar Depression Through the Application of a Model-Based Algorithm in Terms of Clinical Feature and Peripheral Biomarkers
The nature of the diagnostic classification of mood disorder is a typical dichotomous data problem and the method of combining different dimensions of evidences to make judgments might be more statistically reliable. In this paper, we aimed to explore whether peripheral neurotrophic factors could be...
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| Published in | Frontiers in psychiatry Vol. 10; p. 266 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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Frontiers Media S.A
01.05.2019
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| ISSN | 1664-0640 1664-0640 |
| DOI | 10.3389/fpsyt.2019.00266 |
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| Abstract | The nature of the diagnostic classification of mood disorder is a typical dichotomous data problem and the method of combining different dimensions of evidences to make judgments might be more statistically reliable. In this paper, we aimed to explore whether peripheral neurotrophic factors could be helpful for early detection of bipolar depression.
A screening method combining peripheral biomarkers and clinical characteristics was applied in 30 patients with major depressive disorder (MDD) and 23 patients with depressive episode of bipolar disorder. By a model-based algorithm, some information was extracted from the dataset and used as a "model" to approach penalized regression model for stably differential diagnosis for bipolar depression.
A simple and efficient model of approaching the diagnosis of individuals with depressive symptoms was established with a fitting degree (90.58%) and an acceptable cross-validation error rate. Neurotrophic factors of our interest were successfully screened out from the feature selection and optimized model performance as reliable predictive variables.
It seems to be feasible to combine different types of clinical characteristics with biomarkers in order to detect bipolarity of all depressive episodes. Neurotrophic factors of our interest presented its stable discriminant potentiality in unipolar and bipolar depression, deserving validation analysis in larger samples. |
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| AbstractList | The nature of the diagnostic classification of mood disorder is a typical dichotomous data problem and the method of combining different dimensions of evidences to make judgments might be more statistically reliable. In this paper, we aimed to explore whether peripheral neurotrophic factors could be helpful for early detection of bipolar depression.
A screening method combining peripheral biomarkers and clinical characteristics was applied in 30 patients with major depressive disorder (MDD) and 23 patients with depressive episode of bipolar disorder. By a model-based algorithm, some information was extracted from the dataset and used as a "model" to approach penalized regression model for stably differential diagnosis for bipolar depression.
A simple and efficient model of approaching the diagnosis of individuals with depressive symptoms was established with a fitting degree (90.58%) and an acceptable cross-validation error rate. Neurotrophic factors of our interest were successfully screened out from the feature selection and optimized model performance as reliable predictive variables.
It seems to be feasible to combine different types of clinical characteristics with biomarkers in order to detect bipolarity of all depressive episodes. Neurotrophic factors of our interest presented its stable discriminant potentiality in unipolar and bipolar depression, deserving validation analysis in larger samples. Objectives: The nature of the diagnostic classification of mood disorder is a typical dichotomous data problem and the method of combining different dimensions of evidences to make judgments might be more statistically reliable. In this paper, we aimed to explore whether peripheral neurotrophic factors could be helpful for early detection of bipolar depression.Methods: A screening method combining peripheral biomarkers and clinical characteristics was applied in 30 patients with major depressive disorder (MDD) and 23 patients with depressive episode of bipolar disorder. By a model-based algorithm, some information was extracted from the dataset and used as a “model” to approach penalized regression model for stably differential diagnosis for bipolar depression.Results: A simple and efficient model of approaching the diagnosis of individuals with depressive symptoms was established with a fitting degree (90.58%) and an acceptable cross-validation error rate. Neurotrophic factors of our interest were successfully screened out from the feature selection and optimized model performance as reliable predictive variables.Conclusion: It seems to be feasible to combine different types of clinical characteristics with biomarkers in order to detect bipolarity of all depressive episodes. Neurotrophic factors of our interest presented its stable discriminant potentiality in unipolar and bipolar depression, deserving validation analysis in larger samples. Objectives: The nature of the diagnostic classification of mood disorder is a typical dichotomous data problem and the method of combining different dimensions of evidences to make judgments might be more statistically reliable. In this paper, we aimed to explore whether peripheral neurotrophic factors could be helpful for early detection of bipolar depression. Methods: A screening method combining peripheral biomarkers and clinical characteristics was applied in 30 patients with major depressive disorder (MDD) and 23 patients with depressive episode of bipolar disorder. By a model-based algorithm, some information was extracted from the dataset and used as a “model” to approach penalized regression model for stably differential diagnosis for bipolar depression. Results: A simple and efficient model of approaching the diagnosis of individuals with depressive symptoms was established with a fitting degree (90.58%) and an acceptable cross-validation error rate. Neurotrophic factors of our interest were successfully screened out from the feature selection and optimized model performance as reliable predictive variables. Conclusion: It seems to be feasible to combine different types of clinical characteristics with biomarkers in order to detect bipolarity of all depressive episodes. Neurotrophic factors of our interest presented its stable discriminant potentiality in unipolar and bipolar depression, deserving validation analysis in larger samples. Objectives: The nature of the diagnostic classification of mood disorder is a typical dichotomous data problem and the method of combining different dimensions of evidences to make judgments might be more statistically reliable. In this paper, we aimed to explore whether peripheral neurotrophic factors could be helpful for early detection of bipolar depression. Methods: A screening method combining peripheral biomarkers and clinical characteristics was applied in 30 patients with major depressive disorder (MDD) and 23 patients with depressive episode of bipolar disorder. By a model-based algorithm, some information was extracted from the dataset and used as a "model" to approach penalized regression model for stably differential diagnosis for bipolar depression. Results: A simple and efficient model of approaching the diagnosis of individuals with depressive symptoms was established with a fitting degree (90.58%) and an acceptable cross-validation error rate. Neurotrophic factors of our interest were successfully screened out from the feature selection and optimized model performance as reliable predictive variables. Conclusion: It seems to be feasible to combine different types of clinical characteristics with biomarkers in order to detect bipolarity of all depressive episodes. Neurotrophic factors of our interest presented its stable discriminant potentiality in unipolar and bipolar depression, deserving validation analysis in larger samples.Objectives: The nature of the diagnostic classification of mood disorder is a typical dichotomous data problem and the method of combining different dimensions of evidences to make judgments might be more statistically reliable. In this paper, we aimed to explore whether peripheral neurotrophic factors could be helpful for early detection of bipolar depression. Methods: A screening method combining peripheral biomarkers and clinical characteristics was applied in 30 patients with major depressive disorder (MDD) and 23 patients with depressive episode of bipolar disorder. By a model-based algorithm, some information was extracted from the dataset and used as a "model" to approach penalized regression model for stably differential diagnosis for bipolar depression. Results: A simple and efficient model of approaching the diagnosis of individuals with depressive symptoms was established with a fitting degree (90.58%) and an acceptable cross-validation error rate. Neurotrophic factors of our interest were successfully screened out from the feature selection and optimized model performance as reliable predictive variables. Conclusion: It seems to be feasible to combine different types of clinical characteristics with biomarkers in order to detect bipolarity of all depressive episodes. Neurotrophic factors of our interest presented its stable discriminant potentiality in unipolar and bipolar depression, deserving validation analysis in larger samples. |
| Author | Shi, Shenxun Lin, Zhiguang Fang, Yiru He, Shen Jiang, Kaida Liu, Xiaohua Zheng, Yanqun Zhang, Tianhong |
| AuthorAffiliation | 3 Biochemistry Laboratory, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai , China 4 Department of Psychiatry, Huashan Hospital affiliated to Fudan University , Shanghai , China 1 Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai , China 2 Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai , China |
| AuthorAffiliation_xml | – name: 2 Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai , China – name: 4 Department of Psychiatry, Huashan Hospital affiliated to Fudan University , Shanghai , China – name: 1 Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai , China – name: 3 Biochemistry Laboratory, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai , China |
| Author_xml | – sequence: 1 givenname: Yanqun surname: Zheng fullname: Zheng, Yanqun – sequence: 2 givenname: Shen surname: He fullname: He, Shen – sequence: 3 givenname: Tianhong surname: Zhang fullname: Zhang, Tianhong – sequence: 4 givenname: Zhiguang surname: Lin fullname: Lin, Zhiguang – sequence: 5 givenname: Shenxun surname: Shi fullname: Shi, Shenxun – sequence: 6 givenname: Yiru surname: Fang fullname: Fang, Yiru – sequence: 7 givenname: Kaida surname: Jiang fullname: Jiang, Kaida – sequence: 8 givenname: Xiaohua surname: Liu fullname: Liu, Xiaohua |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31118905$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_neubiorev_2022_104552 crossref_primary_10_1016_j_psychres_2020_113319 crossref_primary_10_1186_s12888_021_03548_w crossref_primary_10_1016_j_bios_2022_114842 crossref_primary_10_3390_jpm11020114 crossref_primary_10_1038_s41398_020_01181_x crossref_primary_10_1097_HRP_0000000000000356 crossref_primary_10_17116_jnevro2024124091104 |
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| Keywords | bipolar depression model-based algorithm biomarker neurotrophic factor clinical feature |
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| Title | Detection Study of Bipolar Depression Through the Application of a Model-Based Algorithm in Terms of Clinical Feature and Peripheral Biomarkers |
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