The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review
<h3>Background</h3><p dir="ltr">Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predomi...
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2021
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| _version_ | 1864513516631752704 |
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| author | Zainab Jan (17306614) |
| author2 | Noor AI-Ansari (18520038) Osama Mousa (18288898) Alaa Abd-alrazaq (17058018) Arfan Ahmed (17541309) Tanvir Alam (638619) Mowafa Househ (9154124) |
| author2_role | author author author author author author |
| author_facet | Zainab Jan (17306614) Noor AI-Ansari (18520038) Osama Mousa (18288898) Alaa Abd-alrazaq (17058018) Arfan Ahmed (17541309) Tanvir Alam (638619) Mowafa Househ (9154124) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zainab Jan (17306614) Noor AI-Ansari (18520038) Osama Mousa (18288898) Alaa Abd-alrazaq (17058018) Arfan Ahmed (17541309) Tanvir Alam (638619) Mowafa Househ (9154124) |
| dc.date.none.fl_str_mv | 2021-11-19T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.2196/29749 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/The_Role_of_Machine_Learning_in_Diagnosing_Bipolar_Disorder_Scoping_Review/25771968 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning machine learning bipolar disorder diagnosis support vector machine clinical data mental health scoping review |
| dc.title.none.fl_str_mv | The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.</p><h3>Objective</h3><p dir="ltr">This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.</p><h3>Methods</h3><p dir="ltr">The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.</p><h3>Results</h3><p dir="ltr">We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.</p><h3>Conclusions</h3><p dir="ltr">This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Medical Internet Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/29749" target="_blank">https://dx.doi.org/10.2196/29749</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_aa57fce9b5b8d608afc37195a17f5d4c |
| identifier_str_mv | 10.2196/29749 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25771968 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping ReviewZainab Jan (17306614)Noor AI-Ansari (18520038)Osama Mousa (18288898)Alaa Abd-alrazaq (17058018)Arfan Ahmed (17541309)Tanvir Alam (638619)Mowafa Househ (9154124)Biomedical and clinical sciencesClinical sciencesHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningmachine learningbipolar disorderdiagnosissupport vector machineclinical datamental healthscoping review<h3>Background</h3><p dir="ltr">Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.</p><h3>Objective</h3><p dir="ltr">This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.</p><h3>Methods</h3><p dir="ltr">The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.</p><h3>Results</h3><p dir="ltr">We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.</p><h3>Conclusions</h3><p dir="ltr">This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Medical Internet Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/29749" target="_blank">https://dx.doi.org/10.2196/29749</a></p>2021-11-19T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.2196/29749https://figshare.com/articles/journal_contribution/The_Role_of_Machine_Learning_in_Diagnosing_Bipolar_Disorder_Scoping_Review/25771968CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/257719682021-11-19T03:00:00Z |
| spellingShingle | The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review Zainab Jan (17306614) Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning machine learning bipolar disorder diagnosis support vector machine clinical data mental health scoping review |
| status_str | publishedVersion |
| title | The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review |
| title_full | The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review |
| title_fullStr | The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review |
| title_full_unstemmed | The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review |
| title_short | The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review |
| title_sort | The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review |
| topic | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning machine learning bipolar disorder diagnosis support vector machine clinical data mental health scoping review |