A multimodal prediction model for suicidal attempter in major depressive disorder
Background Suicidal attempts in patients with major depressive disorder (MDD) have become an important challenge in global mental health affairs. To correctly distinguish MDD patients with and without suicidal attempts, a multimodal prediction model was developed in this study using multimodality da...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-11-01
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| Online Access: | https://peerj.com/articles/16362.pdf |
| _version_ | 1827604624531521536 |
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| author | Qiaojun Li Kun Liao |
| author_facet | Qiaojun Li Kun Liao |
| author_sort | Qiaojun Li |
| collection | DOAJ |
| description | Background Suicidal attempts in patients with major depressive disorder (MDD) have become an important challenge in global mental health affairs. To correctly distinguish MDD patients with and without suicidal attempts, a multimodal prediction model was developed in this study using multimodality data, including demographic, depressive symptoms, and brain structural imaging data. This model will be very helpful in the early intervention of MDD patients with suicidal attempts. Methods Two feature selection methods, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms, were merged for feature selection in 208 MDD patients. SVM was then used as a classification model to distinguish MDD patients with suicidal attempts or not. Results The multimodal predictive model was found to correctly distinguish MDD patients with and without suicidal attempts using integrated features derived from SVM-RFE and RF, with a balanced accuracy of 77.78%, sensitivity of 83.33%, specificity of 70.37%, positive predictive value of 78.95%, and negative predictive value of 76.00%. The strategy of merging the features from two selection methods outperformed traditional methods in the prediction of suicidal attempts in MDD patients, with hippocampal volume, cerebellar vermis volume, and supracalcarine volume being the top three features in the prediction model. Conclusions This study not only developed a new multimodal prediction model but also found three important brain structural phenotypes for the prediction of suicidal attempters in MDD patients. This prediction model is a powerful tool for early intervention in MDD patients, which offers neuroimaging biomarker targets for treatment in MDD patients with suicidal attempts. |
| first_indexed | 2024-03-09T06:03:44Z |
| format | Article |
| id | doaj.art-d648fa91f09240a090195d1047a43ed9 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:03:44Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-d648fa91f09240a090195d1047a43ed92023-12-03T12:06:57ZengPeerJ Inc.PeerJ2167-83592023-11-0111e1636210.7717/peerj.16362A multimodal prediction model for suicidal attempter in major depressive disorderQiaojun Li0Kun Liao1College of Information Engineering, Tianjin University of Commerce, Tianjin, ChinaCollege of Sciences, Tianjin University of Commerce, Tianjin, ChinaBackground Suicidal attempts in patients with major depressive disorder (MDD) have become an important challenge in global mental health affairs. To correctly distinguish MDD patients with and without suicidal attempts, a multimodal prediction model was developed in this study using multimodality data, including demographic, depressive symptoms, and brain structural imaging data. This model will be very helpful in the early intervention of MDD patients with suicidal attempts. Methods Two feature selection methods, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms, were merged for feature selection in 208 MDD patients. SVM was then used as a classification model to distinguish MDD patients with suicidal attempts or not. Results The multimodal predictive model was found to correctly distinguish MDD patients with and without suicidal attempts using integrated features derived from SVM-RFE and RF, with a balanced accuracy of 77.78%, sensitivity of 83.33%, specificity of 70.37%, positive predictive value of 78.95%, and negative predictive value of 76.00%. The strategy of merging the features from two selection methods outperformed traditional methods in the prediction of suicidal attempts in MDD patients, with hippocampal volume, cerebellar vermis volume, and supracalcarine volume being the top three features in the prediction model. Conclusions This study not only developed a new multimodal prediction model but also found three important brain structural phenotypes for the prediction of suicidal attempters in MDD patients. This prediction model is a powerful tool for early intervention in MDD patients, which offers neuroimaging biomarker targets for treatment in MDD patients with suicidal attempts.https://peerj.com/articles/16362.pdfMDDSuicidal attemptsMachine learningSVM-RFERFSupport vector machine |
| spellingShingle | Qiaojun Li Kun Liao A multimodal prediction model for suicidal attempter in major depressive disorder PeerJ MDD Suicidal attempts Machine learning SVM-RFE RF Support vector machine |
| title | A multimodal prediction model for suicidal attempter in major depressive disorder |
| title_full | A multimodal prediction model for suicidal attempter in major depressive disorder |
| title_fullStr | A multimodal prediction model for suicidal attempter in major depressive disorder |
| title_full_unstemmed | A multimodal prediction model for suicidal attempter in major depressive disorder |
| title_short | A multimodal prediction model for suicidal attempter in major depressive disorder |
| title_sort | multimodal prediction model for suicidal attempter in major depressive disorder |
| topic | MDD Suicidal attempts Machine learning SVM-RFE RF Support vector machine |
| url | https://peerj.com/articles/16362.pdf |
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