The role of cognitive functions in the diagnosis of bipolar disorder: A machine learning model

Sonkurt, Harun Olcay; Altınöz, Ali Ercan; Çimen, Emre; Köşger, Ferdi; Öztürk, Gürkan

doi:10.1016/j.ijmedinf.2020.104311

Cited by 8 publications

(15 citation statements)

References 34 publications

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“…Among the 33 included articles, 30 were research articles (91%) [ 14 , 18 - 46 ], whereas 3 articles were conference proceedings (9%) [ 24 , 42 , 47 ], as shown in Table 1 and Multimedia Appendix 4 . Articles were published in 14 different countries; China (8, 24%) [ 14 , 18 - 20 , 22 , 25 , 30 , 39 ], India (1, 3%) [ 21 ], Germany (2, 6%) [ 23 , 47 ], United Kingdom (1, 3%) [ 26 ], United States (8, 24%) [ 27 , 28 , 32 , 34 , 37 , 38 , 41 , 45 ], Korea (2, 6%) [ 29 , 36 ], Egypt (1, 3%) [ 31 ], Turkey (2, 6%) [ 31 , 43 ], Italy (1, 3%) [ 33 ], Brazil (1%) [ 47 ], Australia (1%) [ 35 ], the Netherlands (1, 3%) [ 36 ], Norway (1, 3%) [ 37 ], Canada (1, 3%) [ 40 ] and Japan (1, 3%) [ 46 ]; however, the highest numbers of articles were from China and the United States, as observed in Figure 2 . The highest numbers of the articles were published in 2018 and 2019 (7, 21.21%), as shown in Figure 2 and Multimedia Appendices 5 and 6 .…”

Section: Resultsmentioning

confidence: 99%

“…As shown in Table 4 and Multimedia Appendix 4 , clinical and nonclinical data are reported in the included articles. The following data sets were collected from various public and private sources: clinical data (immune-inflammatory signature, blood sample, neuropsychological, neurocognitive, electroencephalography and PGBI-10M manic symptom data) [ 18 - 28 , 30 , 31 , 33 , 38 , 41 - 43 , 46 , 49 ] and nonclinical data (Cambridge Neuropsychological Test Automated Battery cognitive scores, microarray expression data sets, large-scale GWAS, fractional anisotropy, axial diffusivity, radial diffusivity, electronic medical records, bipolarity indices, affective disorder evaluation scale, daily mood ratings survey and phenotypic data sets) [ 14 , 19 , 21 , 24 , 29 , 32 , 34 , 36 , 37 , 45 , 47 , 49 , 50 ].…”

Section: Resultsmentioning

confidence: 99%

“…The accuracy level ranged from ≤70% to >91%. The accuracy level was ≤70% in 3 studies [ 25 , 32 , 36 ], 71%-78% in 7 studies [ 18 , 23 , 34 , 37 , 43 , 47 , 49 ], 83%-90% in 9 studies [ 14 , 20 , 21 , 26 , 28 , 29 , 31 , 33 , 42 ], and >91% in 5 studies [ 19 , 22 , 24 , 35 , 40 ]. The highest accuracy was 98%, found in only 1 study, whereas the lowest accuracy level was 64%.…”

Section: Resultsmentioning

confidence: 99%

“…The proportion of the area under the curve (AUC) value was only reported in 10 studies, ranging from ≤69% to >97%. In 3 studies, the AUC ratio was ≤70% [ 23 , 36 , 45 ]; in 2 studies, it was 74%-78% [ 31 , 43 ] and 84%-88% [ 14 , 47 ], and in 3 studies [ 19 , 28 , 33 ], it was >90%. The maximum AUC ratio was 97%, whereas the minimum AUC value was 65%.…”

Section: Resultsmentioning

confidence: 99%

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The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

Jan¹,

Ai-Ansari²,

Mousa³

et al. 2021

J Med Internet Res

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Background 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. Objective This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes. Methods 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. Results 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%. Conclusions 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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

Jan¹,

Ai-Ansari²,

Mousa³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…And the classifier must be refined to capture the peculiar nature of the online diagnostic problem. The existing literature already provides us some candidate algorithms such as random forest [1], neural network [2], and other methods [3][4][5], and they have been widely used in medical diagnosis. However, in general, these algorithms often yield either an interminable training and response time or an unbearably low diagnostic accuracy and privacy protection.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection

Zhang

Zheng

Wang

2021

Wireless Communications and Mobile Computing

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Medical prediagnosis systems are now available online to give users quick and preliminary diagnosis information. The need for such a system has become particularly evident in areas with insufficient health professionals. Due to the privacy of patient medical information and the sensitivity of cloud diagnosis models, it is necessary to protect the security of data, models, and communications. These existing diagnosis systems can hardly provide a satisfied diagnosis accuracy while ensuring comprehensive security and high efficiency. In order to solve these problems, we proposed Relief- k minimum Wasserstein distance (Relief- k MW) classification method, which combined data encryption and BLS signature to form a privacy-preserving efficient online multiparty interactive medical prediagnostic scheme (OMPD). Theoretical analysis shows our OMPD effectively provides high-precision prediagnosis services. Extensive experimental results demonstrate that OMPD not only greatly improves the diagnostic accuracy but also reduces the computational and communication overhead.

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Stress-Related Exhaustion, Polygenic Cognitive Potential, and Cognitive Test Performance – A General Population Study

et al. 2023

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Background We investigated whether stress-related exhaustion (chronic or short-term, and co-occurring with depression or not) is related to cognitive performance and whether polygenic cognitive potential modifies these associations. Methods The participants were from the Young Finns Study (N = 541–1273). Stress-related exhaustion was assessed using the Maastricht Questionnaire, depressive symptoms with the Beck Depression Inventory, and cognitive performance with subtests of the Cambridge Neuropsychological Test Automated Battery, measuring visuospatial learning, reaction time, sustained attention, and executive function. Cognitive performance and depression were assessed in 2011, and exhaustion in 2001, 2007, and 2011. A polygenic score for cognitive potential was calculated based on a GWAS on intelligence. Results High stress-related exhaustion, especially chronic, was associated with slower reaction time. Only clinical levels of depression were related to slower reaction time. Polygenic cognitive potential did not modify these associations. There were no differences in cognitive performance between individuals with co-occurring exhaustion and depression vs. those with only either condition. Conclusion Stress-related exhaustion, especially if chronic, seems to relate to slower reactions. Co-occurring exhaustion and depression may not have additive effects on cognitive performance. High polygenic cognitive potential may not protect from or predispose to harmful effects of exhaustion or depression on reaction time.

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The role of cognitive functions in the diagnosis of bipolar disorder: A machine learning model

Cited by 8 publications

References 34 publications

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection

Stress-Related Exhaustion, Polygenic Cognitive Potential, and Cognitive Test Performance – A General Population Study

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