Bipolar Disorder

A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder.

BACKGROUND: Concomitant use of complementary, multimodal imaging measures and neurocognitive measures is reported to have higher accuracy as a biomarker in Alzheimer's dementia. However, such an approach has not been examined to differentiate healthy individuals from Bipolar disorder. In this study, we examined the utility of support vector machine (SVM) technique to differentiate bipolar disorder...

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a pop...

Predicting individual clinical trajectories of depression with generative embedding.

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients exp...

Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial.

BACKGROUND: Informal caregivers report substantial burden and depressive symptoms which predict higher rates of patient institutionalization. While ca...

Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia.

IMPORTANCE: Little guidance exists to date on how to select antipsychotic medications for patients with first-episode schizophrenia.

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory m...

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database.

INTRODUCTION: Major depressive disorder (MDD) is one of the most common mental disorders worldwide. The aim of this study was to identify potential pa...

Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder.

BACKGROUND: In order to reduce unsuccessful treatment trials for depression, neuroimaging and genetic information can be considered as biomarkers. Tog...

Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.

Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major ...

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.

A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive interventi...

Prediction of lithium response using clinical data.

OBJECTIVE: Promptly establishing maintenance therapy could reduce morbidity and mortality in patients with bipolar disorder. Using a machine learning ...

Implementing machine learning in bipolar diagnosis in China.

Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of th...

Predicting rehospitalization within 2 years of initial patient admission for a major depressive episode: a multimodal machine learning approach.

Machine learning methods show promise to translate univariate biomarker findings into clinically useful multivariate decision support systems. At curr...

ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder.

Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to tak...

A network perspective on body dysmorphic disorder and major depressive disorder.

BACKGROUND: Body dysmorphic disorder (BDD) is a highly debilitating mental disorder associated with notable psychosocial impairment and high rates of ...

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [S]GTPγS Binding Assays.

G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amou...

Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification.

In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that netw...

Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression.

BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations o...

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder.

Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical im...

Simultaneous quantification of ticagrelor and its active metabolite, AR-C124910XX, in human plasma by liquid chromatography-tandem mass spectrometry: Applications in steady-state pharmacokinetics in patients.

A sensitive and simple liquid chromatography-tandem mass spectrometry method was developed and validated for the simultaneous quantification of ticagr...