Back to search

Article

Machine Learning Predicts Treatment Response in Bipolar & Major Depressive Disorders

2022-05-17

Abstract excerpt

Diagnosis of bipolar disorder (BD) patients with complex symptoms presents a challenge to clinicians. Patients tend to spend more time in a depressive state than a manic state. In such complex cases, the current Diagnostic and Statistical Manual (DSM), which is not based on pathophysiology, can lead to misdiagnosis as major depressive disorder (MDD) and an imperfect or even harmful medication response. A biologica...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
7a3e73c1-f555-5161-8fed-d112d9a0e161
DOI
10.1101/2022.05.13.491903
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Machine Learning Predicts Treatment Response in Bipolar & Major Depressive DisordersDOI 10.1101/2022.05.13.491903
Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysisSemantic proximity
Prediction of response to transcranial magnetic stimulation treatment for depression using electroencephalography and statistical learning methods, including an out-of-sample validationSemantic proximity
Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysisSemantic proximity
Machine Learning-Enabled EEG Biomarkers Predict Divergent Antidepressant and Placebo Response in a Clinical Trial of Major DepressionSemantic proximity
Template-driven dynamic functional network connectivity predicts medication response for major depression and bipolar disordersSemantic proximity
Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasetsSemantic proximity
Electroencephalography for predicting antidepressant treatment success: a systematic review and meta-analysisSemantic proximity
Advancing Early Detection of Major Depressive Disorder: A Comparative Analysis of AI Models Using Multi-Site Functional MRI DataSemantic proximity
Optimizing Antidepressant Efficacy: Generalizable Multimodal Neuroimaging Biomarkers for Prediction of Treatment ResponseSemantic proximity
Prediction of antidepressant treatment response with thalamo-somatomotor functional connectivity revealed by generalizable stratification of depressed patientsSemantic proximity
Are there meaningful biomarkers of treatment response for depression?Semantic proximity
The impact of machine learning techniques in the study of bipolar disorder: A systematic review.Semantic proximity
Select a neighboring publication to make it the new centre.