Mental Disorder Detection : Bipolar Disorder Scrutinization Using Machine Learning

2019 ◽  
Author(s):  
Ranjana Jadhav ◽  
Vinay Chellwani ◽  
Sharyu Deshmukh ◽  
Hitesh Sachdev
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Mental Disorder

Early Detection of Bipolar Disorder Four Years Before Onset in a 22-Year Population-Based Birth Cohort Using Advanced Machine Learning Techniques

2020 ◽  
Author(s):  
Francisco Diego Rabelo-da-Ponte ◽  
Jacson Gabriel Feiten ◽  
Benson Mwangi ◽  
Fernando C. Barros ◽  
Fernando C. Wehrmeister ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Early Detection ◽  
Birth Cohort ◽  
Population Based ◽  
Machine Learning Techniques ◽  
Learning Techniques

Application of machine learning in diagnostic value of mRNAs for bipolar disorder

2021 ◽  
pp. 1-8
Author(s):  
Xulong Wu ◽  
Lulu Zhu ◽  
Zhi Zhao ◽  
Bingyi Xu ◽  
Jialei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Diagnostic Value

Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects

NeuroImage ◽  
2014 ◽  
Vol 84 ◽  
pp. 299-306 ◽  
Author(s):  
Hugo G. Schnack ◽  
Mireille Nieuwenhuis ◽  
Neeltje E.M. van Haren ◽  
Lucija Abramovic ◽  
Thomas W. Scheewe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Healthy Subjects ◽  
Structural Mri ◽  
Learning Study ◽  
Clinical Classification

scholarly journals Highly Predictive Transdiagnostic Features Shared across Schizophrenia, Bipolar Disorder, and ADHD Identified Using a Machine Learning Based Approach

2018 ◽  
Author(s):  
Yuelu Liu ◽  
Monika S. Mellem ◽  
Humberto Gonzalez ◽  
Matthew Kollada ◽  
Atul R. Mahableshwarkar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Patient Group ◽  
Attention Deficit ◽  
The United States ◽  
Self Report ◽  
Healthy Controls ◽  
Cohen’S D ◽  
Cohen's D

AbstractThe Diagnostic and Statistical Manual of Mental Disorders (DSM) is the standard for diagnosing psychiatric disorders in the United States. However, evidence has suggested that symptoms in psychiatric disorders are not restricted to the boundaries between DSM categories, implying an underlying latent transdiagnostic structure of psychopathology. Here, we applied an importance-guided machine learning technique for model selection to item-level data from self-reported instruments contained within the Consortium for Neuropsychiatric Phenomics dataset. From 578 questionnaire items, we identified a set of features which consisted of 85 items that were shared across diagnoses of schizophrenia (SCZ), bipolar disorder (BD), and attention deficit/hyperactivity disorder (ADHD). A classifier trained on the transdiagnostic features reliably distinguished the patient group as a whole from healthy controls (classification AUC = 0.95) and only 10 items were needed to attain the performance level of AUC being 0.90. A sum score created from the items produced high separability between patients and healthy controls (Cohen’s d = 2.85), and it outperformed predefined sum scores and sub-scores within the instruments (Cohen’s d ranging between 0.13 and 1.21). The transdiagnostic features comprised both symptom domains (e.g. dysregulated mood, attention deficit, and anhedonia) and personality traits (e.g. neuroticism, impulsivity, and extraversion). Moreover, by comparing the features that were common across the three patient groups with those that were most predictive of a single patient category, we can describe the unique features for each patient group superimposed on the transdiagnostic feature structure. Overall, our results reveal a latent transdiagnostic symptom/behavioral phenotypic structure shared across SCZ, BD, and ADHD and present a new perspective to understand insights offered by self-report psychiatric instruments.

scholarly journals Major depressive disorder, suicidal behaviour, bipolar disorder, and generalised anxiety disorder among emerging adults with and without chronic health conditions

2015 ◽  
Vol 25 (5) ◽  
pp. 462-474 ◽  
Author(s):  
M. A. Ferro
Keyword(s):  
Bipolar Disorder ◽  
Mental Disorder ◽  
Emerging Adults ◽  
Chronic Health Condition ◽  
Health Condition ◽  
Chronic Health Conditions ◽  
Health Conditions ◽  
Generalised Anxiety Disorder ◽  
Major Depressive ◽  
Chronic Health

Background.Despite the considerable physical, emotional and social change that occurs during emerging adulthood, there is little research that examines the association between having a chronic health condition and mental disorder during this developmental period. The aims of this study were to examine the sex-specific prevalence of lifetime mental disorder in an epidemiological sample of emerging adults aged 15–30 years with and without chronic health conditions; quantify the association between chronic health conditions and mental disorder, adjusting for sociodemographic and health factors; and, examine potential moderating and mediating effects of sex, level of disability and pain.Method.Data come from the Canadian Community Health Survey-Mental Health. Respondents were 15–30 years of age (n = 5947) and self-reported whether they had a chronic health condition. Chronic health conditions were classified as: respiratory, musculoskeletal/connective tissue, cardiovascular, neurological and endocrine/digestive. The World Health Organization Composite International Diagnostic Interview 3.0 was used to assess the presence of mental disorder (major depressive disorder, suicidal behaviour, bipolar disorder and generalised anxiety disorder).Results.Lifetime prevalence of mental disorder was significantly higher for individuals with chronic health conditions compared with healthy controls. Substantial heterogeneity in the prevalence of mental disorder was found in males, but not in females. Logistic regression models adjusting for several sociodemographic and health factors showed that the individuals with chronic health conditions were at elevated risk for mental disorder. There was no evidence that the level of disability or pain moderated the associations between chronic health conditions and mental disorder. Sex was found to moderate the association between musculoskeletal/connective tissue conditions and bipolar disorder (β = 1.71, p = 0.002). Exploratory analyses suggest that the levels of disability and pain mediate the association between chronic health conditions and mental disorder.Conclusions.Physical and mental comorbidity is prevalent among emerging adults and this relationship is not augmented, but may be mediated, by the level of disability or pain. Findings point to the integration and coordination of public sectors – health, education and social services – to facilitate the prevention and reduction of mental disorder among emerging adults with chronic health conditions.

scholarly journals The incidence, psychiatric co-morbidity and pharmacological treatment of severe mental disorders in children and adolescents

2018 ◽  
Vol 49 ◽  
pp. 16-22 ◽  
Author(s):  
Ragnar Nesvåg ◽  
Jørgen G. Bramness ◽  
Marte Handal ◽  
Ingeborg Hartz ◽  
Vidar Hjellvik ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Mental Disorders ◽  
Mental Disorder ◽  
Children And Adolescents ◽  
Pharmacological Treatment ◽  
Depressive Episode ◽  
Psychotic Symptoms ◽  
Severe Mental Disorder ◽  
Co Morbidity ◽  
First Time

AbstractBackgroundAntipsychotic drug use among children and adolescents is increasing, and there is growing concern about off-label use and adverse effects. The present study aims to investigate the incidence, psychiatric co-morbidity and pharmacological treatment of severe mental disorder in Norwegian children and adolescents.MethodsWe obtained data on mental disorders from the Norwegian Patient Registry on 0–18 year olds who during 2009–2011 were diagnosed for the first time with schizophrenia-like disorder (International Classification of Diseases, 10th revision codes F20-F29), bipolar disorder (F30-F31), or severe depressive episode with psychotic symptoms (F32.3 or F33.3). Data on filled prescriptions for psychotropic drugs were obtained from the Norwegian Prescription Database.ResultsA total of 884 children and adolescents (25.1 per 100 000 person years) were first time diagnosed with schizophrenia-like disorder (12.6 per 100 000 person years), bipolar disorder (9.2 per 100 000 person years), or severe depressive episode with psychotic symptoms (3.3 per 100 000 person years) during 2009–2011. The most common co-morbid mental disorders were depressive (38.1%) and anxiety disorders (31.2%). Antipsychotic drugs were prescribed to 62.4% of the patients, 72.0% of the schizophrenia-like disorder patients, 51.7% of the bipolar disorder patients, and 55.4% of the patients with psychotic depression. The most commonly prescribed drugs were quetiapine (29.5%), aripiprazole (19.6%), olanzapine (17.3%), and risperidone (16.6%).ConclusionsWhen a severe mental disorder was diagnosed in children and adolescents, the patient was usually also prescribed antipsychotic medication. Clinicians must be aware of the high prevalence of depressive and anxiety disorders among early psychosis patients.

scholarly journals Intellectual disability and mental disorders in a US population representative sample of adolescents

2018 ◽  
Vol 49 (6) ◽  
pp. 952-961 ◽  
Author(s):  
Jonathan M. Platt ◽  
Katherine M. Keyes ◽  
Katie A. McLaughlin ◽  
Alan S. Kaufman
Keyword(s):  
Bipolar Disorder ◽  
Intellectual Disability ◽  
Mental Disorders ◽  
Mental Disorder ◽  
Behavioral Problems ◽  
Behavior Disorders ◽  
Representative Sample ◽  
Age Of Onset ◽  
Population Sample ◽  
Clinical Samples

AbstractBackgroundMost research on the prevalence, distribution, and psychiatric comorbidity of intellectual disability (ID) relies on clinical samples, limiting the generalizability and utility of ID assessment in a legal context. This study assessed ID prevalence in a population-representative sample of US adolescents and examined associations of ID with socio-demographic factors and mental disorders.MethodsData were drawn from the National Comorbidity Survey Adolescent Supplement (N= 6256). ID was defined as: (1) IQ ⩽ 76, measured using the Kaufman Brief Intelligence Test; (2) an adaptive behavior score ⩽76, and (3) age of onset ⩽18 measured using a validated scale. The Composite International Diagnostic Interview assessed 15 lifetime mental disorders. The Sheehan disability scale assessed disorder severity. We used logistic regression models to estimate differences in lifetime disorders for adolescents with and without ID.ResultsID prevalence was 3.2%. Among adolescents with ID, 65.1% met lifetime criteria for a mental disorder. ID status was associated with specific phobia, agoraphobia, and bipolar disorder, but not behavior disorders after adjustment for socio-demographics. Adolescents with ID and mental disorders were significantly more likely to exhibit severe impairment than those without ID.ConclusionsThese findings highlight how sample selection and overlap between ID and psychopathology symptoms might bias understanding of the mental health consequences of ID. For example, associations between ID and behavior disorders widely reported in clinical samples were not observed in a population-representative sample after adjustment for socio-demographic confounders. Valid assessment and understanding of these constructs may prove influential in the legal system by influencing treatment referrals and capital punishment decisions.General Scientific SummaryCurrent definitions of intellectual disability (ID) are based on three criteria: formal designation of low intelligence through artificial problem-solving tasks, impairment in one's ability to function in his/her social environment, and early age of onset. In a national population sample of adolescents, the majority of those with ID met criteria for a lifetime mental disorder. Phobias and bipolar disorder, but not behavior disorders, were elevated in adolescents with ID. Findings highlight the need to consider how behavioral problems are conceptualized and classified in people with ID.

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