Bipolar Disorder

Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis.

BACKGROUND: Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network.

Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in th...

Machine learning model for predicting Major Depressive Disorder using RNA-Seq data: optimization of classification approach.

Considering human brain disorders, Major Depressive Disorder (MDD) is seen as a lethal disease in which a person goes to the extent of suicidal behavi...

Repurposing non-oncology small-molecule drugs to improve cancer therapy: Current situation and future directions.

Drug repurposing or repositioning has been well-known to refer to the therapeutic applications of a drug for another indication other than it was orig...

I, robot: depression plays different roles in human-human and human-robot interactions.

Socially engaging robots have been increasingly applied to alleviate depressive symptoms and to improve the quality of social life among different pop...

Robot Assisted Distal Pancreatectomy with Celiac Axis Resection (DP-CAR) for Pancreatic Cancer: Surgical Planning and Technique.

Malignant pancreatic tumors involving the celiac artery can be resected with a distal pancreatectomy, splenectomy and celiac axis resection (DP-CAR), ...

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.

Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive sympt...

Development and validation of the metric-based assessment of a robotic vessel dissection, vessel loop positioning, clip applying and bipolar coagulation task on an avian model.

The evolution of robotic technology and its diffusion does not seem to have been adequately accompanied by the development and implementation of surge...

Retrospective Concomitant Nonrandomized Comparison of "Touch" Cautery Versus Athermal Dissection of the Prostatic Vascular Pedicles and Neurovascular Bundles During Robot-assisted Radical Prostatectomy.

BACKGROUND: During robotic-assisted radical prostatectomy (RARP), the use of electrocautery near the neurovascular bundles (NVBs) frequently results i...

Design and Analysis of a Novel Integral Recurrent Neural Network for Solving Time-Varying Sylvester Equation.

To solve a general time-varying Sylvester equation, a novel integral recurrent neural network (IRNN) is designed and analyzed. This kind of recurrent ...

Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI.

The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD)...

Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1.

OBJECTIVES: Antidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting ...

Quantitative analysis of lithium in brine by laser-induced breakdown spectroscopy based on convolutional neural network.

In this study, a simple and effective method for accurate determination of lithium in brine samples was developed by the combination of laser induced ...

Protein detection enabled using functionalised silk-binding peptides on a silk-coated optical fibre.

We present a new coating procedure to prepare optical fibre sensors suitable for use with protein analytes. We demonstrate this through the detection ...

Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions.

Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizo...

Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity.

The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks....

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critic...

Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times.

Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals' lives, irrespective of being employed or une...

Predicting women with depressive symptoms postpartum with machine learning methods.

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's hea...

Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions.

It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while...