scholarly journals Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Maria Faurholt-Jepsen ◽  
Darius Adam Rohani ◽  
Jonas Busk ◽  
Maj Vinberg ◽  
Jakob Eyvind Bardram ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Machine Learning Algorithms ◽  
Affective States ◽  
Phone Calls ◽  
Healthy Control ◽  
State Marker ◽  
Voice Data ◽  
Increased Activity ◽  
Objective Marker

Abstract Background Voice features have been suggested as objective markers of bipolar disorder (BD). Aims To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD. Methods Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n  = 78.733), UR (n  = 8004), and HC (n  =  20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms. Results Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC  = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC  =  0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC  =  0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC  =  0.67 (SD 0.11). Conclusions Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.

scholarly journals Review on Machine Learning Techniques to predict Bipolar Disorder

2021 ◽  
Author(s):  
Nisha Agnihotri
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Bipolar Disorder ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Brain Disorder ◽  
Research Papers ◽  
Learning Techniques

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>

scholarly journals Review on Machine Learning Techniques to predict Bipolar Disorder

2021 ◽  
Author(s):  
Nisha Agnihotri
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Bipolar Disorder ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Brain Disorder ◽  
Research Papers ◽  
Learning Techniques

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>

Objective smartphone data as a potential diagnostic marker of bipolar disorder

2018 ◽  
Vol 53 (2) ◽  
pp. 119-128 ◽  
Author(s):  
Maria Faurholt-Jepsen ◽  
Jonas Busk ◽  
Helga Þórarinsdóttir ◽  
Mads Frost ◽  
Jakob Eyvind Bardram ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Text Messages ◽  
Repeated Measurements ◽  
Diagnostic Process ◽  
Affective States ◽  
Healthy Individuals ◽  
Phone Calls

Objective: Currently, the diagnosis in bipolar disorder relies on patient information and careful clinical evaluations and judgements with a lack of objective tests. Core clinical features of bipolar disorder include changes in behaviour. We aimed to investigate objective smartphone data reflecting behavioural activities to classify patients with bipolar disorder compared with healthy individuals. Methods: Objective smartphone data were automatically collected from 29 patients with bipolar disorder and 37 healthy individuals. Repeated measurements of objective smartphone data were performed during different affective states in patients with bipolar disorder over 12 weeks and compared with healthy individuals. Results: Overall, the sensitivity of objective smartphone data in patients with bipolar disorder versus healthy individuals was 0.92, specificity 0.39, positive predictive value 0.88 and negative predictive value 0.52. In euthymic patients versus healthy individuals, the sensitivity was 0.90, specificity 0.56, positive predictive value 0.85 and negative predictive value 0.67. In mixed models, automatically generated objective smartphone data (the number of text messages/day, the duration of phone calls/day) were increased in patients with bipolar disorder (during euthymia, depressive and manic or mixed states, and overall) compared with healthy individuals. The amount of time the smartphone screen was ‘on’ per day was decreased in patients with bipolar disorder (during euthymia, depressive state and overall) compared with healthy individuals. Conclusion: Objective smartphone data may represent a potential diagnostic behavioural marker in bipolar disorder and may be a candidate supplementary method to the diagnostic process in the future. Further studies including larger samples, first-degree relatives and patients with other psychiatric disorders are needed.

A peripheral composite proteomic and gene expression biomarker related to diagnosis and affective state in rapid cycling bipolar disorder

2016 ◽  
Vol 33 (S1) ◽  
pp. S123-S123
Author(s):  
K. Munkholm ◽  
M. Vinberg ◽  
L.V. Kessing
Keyword(s):  
Gene Expression ◽  
Bipolar Disorder ◽  
Affective State ◽  
Biological Pathways ◽  
Clinical Test ◽  
Rapid Cycling ◽  
Affective States ◽  
Composite Measure ◽  
Control Subjects ◽  
Healthy Control

IntroductionManagement of bipolar disorder is limited by absence of laboratory test. While alterations related to multiple biological pathways have been found in bipolar disorder, findings have not translated into clinically applicable biomarkers. We previously found promise for a combined gene expression biomarker. The combination of gene expression and proteomic biomarkers could have potential as a meaningful clinical test.ObjectivesTo identify a composite biomarker based on multiple potential peripheral biomarkers related to neuroplasticity, inflammation and oxidative stress, both on a proteomic and gene expression level.AimsTo test the ability of a composite biomarker to discriminate between bipolar disorder patients and healthy control subjects and between affective states in bipolar disorder patients.MethodsmRNA expression of a set of 19 candidate genes and protein levels of immune markers and neurotrophic factors were measured in peripheral blood mononuclear cells and combined with urinary levels of oxidized nucleosides of 37 rapid cycling bipolar disorder patients in different affective states (depression, mania and euthymia) during a 6–12-month period and in 40 age- and gender-matched healthy control subjects. A composite measure was constructed in the first half of the sample and independently validated in the second half of the sample. The composite measure was evaluated using ROC curves and by calculating sensitivity and specificity.ResultsStatistical analysis is ongoing. Results will be presented at the congress.ConclusionsA peripheral composite biomarker based on multiple biological pathways on both proteomic and gene expression levels may have potential as a clinically applicable biomarker.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Implementing machine learning in bipolar diagnosis in China

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yantao Ma ◽  
Jun Ji ◽  
Yun Huang ◽  
Huimin Gao ◽  
Zhiying Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Random Forest ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Linear Discriminant ◽  
Cohort Data ◽  
Selection Operator

AbstractBipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learning to shorten the Affective Disorder Evaluation scale (ADE) based on an analysis of registered Chinese multisite cohort data. In order to evaluate the importance of each item of the ADE, a case-control study of 360 bipolar disorder (BPD) patients, 255 major depressive disorder (MDD) patients and 228 healthy (no psychiatric diagnosis) controls (HCs) was conducted, spanning 9 Chinese health facilities participating in the Comprehensive Assessment and Follow-up Descriptive Study on Bipolar Disorder (CAFÉ-BD). The BDCC was formed by selected items from the ADE according to their importance as calculated by a random forest machine learning algorithm. Five classical machine learning algorithms, namely, a random forest algorithm, support vector regression (SVR), the least absolute shrinkage and selection operator (LASSO), linear discriminant analysis (LDA) and logistic regression, were used to retrospectively analyze the aforementioned cohort data to shorten the ADE. Regarding the area under the receiver operating characteristic (ROC) curve (AUC), the BDCC had high AUCs of 0.948, 0.921, and 0.923 for the diagnosis of MDD, BPD, and HC, respectively, despite containing only 15% (17/113) of the items from the ADE. Traditional scales can be shortened using machine learning analysis. By shortening the ADE using a random forest algorithm, we generated the BDCC, which can be more easily applied in clinical practice to effectively enhance both BPD and MDD diagnosis.

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

2021 ◽  
Author(s):  
Zainab Jan ◽  
Noor AI Ansari 2nd ◽  
Osama Mousa 3rd ◽  
Ala Ali E.Abd-Alrazaq 5th ◽  
Mowafa Househ ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Scoping Review ◽  
Process Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Inclusion Criteria ◽  
Bipolar Patients ◽  
Machine Learning Models

BACKGROUND Bipolar disorder (BD) is the tenth common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. BD patients have 9–17 years lower lifetime as compared to the normal population. It is a predominant mental disorder but misdiagnosed as depressive disorder that leads to difficulties in the treatment of affected patients. 60% of patients with bipolar disorder are looking for the treatment of depression. However, machine learning provides advanced skills and techniques for the better diagnosis of bipolar disorder. OBJECTIVE This review aims to explore the machine learning algorithms for the detection and diagnosis of bipolar disorder and its subtypes. METHODS The study protocol adapts PRISMA extension guidelines. It explores three databases, which were 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, two levels of screening were carried out: the title and abstract review and the 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 573 potential articles were retrieved from three databases. After pre-processing and screening, only 33 articles were identified, which met our inclusion criteria. The most commonly used data belonged to the clinical category (n=22, 66.66%). We identified 8 machine learning models used in the selected studies, Support-vector machines (n=9, 27%), Artificial neural network (n=4, 12.12%) , Linear regression (n=3, 0.9%) , Gaussian process model (n=2, 0.6%), Ensemble model (n=2, 0.6%) , Natural language processing (n=1, 0.3%), Probabilistic Methods (n=1, 0.3%), and Logistic regression (n=1, 0.35%). The most common data utilized was magnetic resonance imaging (MRI) for classifying bipolar patients compared to other groups (n=11, 34%) while the least common utilized data was microarray expression dataset and genomic data. The maximum ratio of accuracy was 98% while the minimum accuracy range was 64%. CONCLUSIONS This scoping review provides an overview of recent studies based on machine learning models used to diagnose bipolar disorder patients regardless of their demographics or if they were assessed compared to patients with psychiatric diagnoses. Further research can be conducted for clinical decision support in the health industry. CLINICALTRIAL Null

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