Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis
Introduction Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively appli...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-03-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241239238 |
| _version_ | 1827316791737581568 |
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| author | Jing Wang Hui Ouyang Runda Jiao Haiyan Zhang Suhui Cheng Zhilei Shang Yanpu Jia Wenjie Yan Lili Wu Weizhi Liu |
| author_facet | Jing Wang Hui Ouyang Runda Jiao Haiyan Zhang Suhui Cheng Zhilei Shang Yanpu Jia Wenjie Yan Lili Wu Weizhi Liu |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | Introduction Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively applied in the realm of mental health. Therefore, our present undertaking seeks to provide a comprehensive assessment of studies employing ML methods that use diverse data modalities on the classification of people with PTSD. Methods and analysis In pursuit of pertinent studies, we will search both English and Chinese databases from January 2000 to May 2022. Two researchers will independently conduct screening, extract data and assess study quality. We intend to employ the assessment framework introduced by Luis Francisco Ramos-Lima in 2020 for quality evaluation. Rate, standard error and 95% CIs will be utilized for effect size measurement. A Cochran's Q test will be applied to assess heterogeneity. Subgroup and sensitivity analysis will further elucidate the source of heterogeneity and funnel plots and Egger's test will detect publication bias. Ethics and dissemination This systematic review and meta-analysis does not encompass patient interactions or engagements with healthcare providers. The outcomes of this research will be disseminated through scholarly channels, including presentations at scientific conferences and publications in peer-reviewed journals. PROSPERO registration number CRD42023342042. |
| first_indexed | 2024-04-24T23:20:38Z |
| format | Article |
| id | doaj.art-7f06c04cccbc40409ea29d47360376cc |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-04-24T23:20:38Z |
| publishDate | 2024-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-7f06c04cccbc40409ea29d47360376cc2024-03-16T09:03:55ZengSAGE PublishingDigital Health2055-20762024-03-011010.1177/20552076241239238Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysisJing Wang0Hui Ouyang1Runda Jiao2Haiyan Zhang3Suhui Cheng4Zhilei Shang5Yanpu Jia6Wenjie Yan7Lili Wu8Weizhi Liu9 The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, , Shanghai, China The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, , Shanghai, China Graduate School, , Beijing, China Department of Health Care, , Shanghai, China The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, , Shanghai, China The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, , Shanghai, China The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, , Shanghai, China The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, , Shanghai, China The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, , Shanghai, China The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, , Shanghai, ChinaIntroduction Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively applied in the realm of mental health. Therefore, our present undertaking seeks to provide a comprehensive assessment of studies employing ML methods that use diverse data modalities on the classification of people with PTSD. Methods and analysis In pursuit of pertinent studies, we will search both English and Chinese databases from January 2000 to May 2022. Two researchers will independently conduct screening, extract data and assess study quality. We intend to employ the assessment framework introduced by Luis Francisco Ramos-Lima in 2020 for quality evaluation. Rate, standard error and 95% CIs will be utilized for effect size measurement. A Cochran's Q test will be applied to assess heterogeneity. Subgroup and sensitivity analysis will further elucidate the source of heterogeneity and funnel plots and Egger's test will detect publication bias. Ethics and dissemination This systematic review and meta-analysis does not encompass patient interactions or engagements with healthcare providers. The outcomes of this research will be disseminated through scholarly channels, including presentations at scientific conferences and publications in peer-reviewed journals. PROSPERO registration number CRD42023342042.https://doi.org/10.1177/20552076241239238 |
| spellingShingle | Jing Wang Hui Ouyang Runda Jiao Haiyan Zhang Suhui Cheng Zhilei Shang Yanpu Jia Wenjie Yan Lili Wu Weizhi Liu Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis Digital Health |
| title | Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis |
| title_full | Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis |
| title_fullStr | Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis |
| title_full_unstemmed | Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis |
| title_short | Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis |
| title_sort | machine learning methods to discriminate posttraumatic stress disorder a protocol of systematic review and meta analysis |
| url | https://doi.org/10.1177/20552076241239238 |
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