AIMC Topic: Depressive Disorder, Treatment-Resistant

Clear Filters Showing 1 to 7 of 7 articles

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Translational psychiatry Apr 27, 2025
Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervise...
Treatment Outcome Supervised Machine Learning Transcranial Magnetic Stimulation Male Middle Aged Depressive Disorder, Treatment-Resistant Humans Retrospective Studies Electronic Health Records Female Aged Adult
View on PubMed DOI

Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.

Clinical EEG and neuroscience Sep 9, 2024
Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Id...
Electroencephalography Antidepressive Agents Depressive Disorder, Treatment-Resistant Deep Learning Depression Neural Networks, Computer Artificial Intelligence Female Adult Male Humans Middle Aged
View on PubMed DOI

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.

PloS one Jun 28, 2021
OBJECTIVES: Antidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict trea...
Female Adult Male Depressive Disorder, Major Canada Treatment Outcome Machine Learning Algorithms Biomarkers Humans Antidepressive Agents Depressive Disorder, Treatment-Resistant Datasets as Topic Clinical Trials as Topic
View on PubMed DOI

Can Machine Learning help us in dealing with treatment resistant depression? A review.

Journal of affective disorders Aug 13, 2019
BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression ...
Depressive Disorder, Treatment-Resistant Machine Learning Antidepressive Agents Algorithms Humans Drug Resistance Adult Female Evidence-Based Practice Male Disease Management
View on PubMed DOI

Differentiating Treatment-Resistant Depression With and Without Parkinsonism in the Elderly From a Psychiatric Perspective by Tc-TRODAT-1 SPECT Imaging.

International journal of geriatric psychiatry Jun 1, 2025
OBJECTIVES: Late-life depression often overlaps with neurodegenerative diseases leading to diagnostic and treatment challenges for neuropsychiatrists. This study aimed to differentiate elderly treatment-resistant depression (TRD) comorbid with parkin...
Diagnosis, Differential Radiopharmaceuticals Parkinsonian Disorders Male Middle Aged Case-Control Studies Dopamine Plasma Membrane Transport Proteins Depressive Disorder, Treatment-Resistant Organotechnetium Compounds Corpus Striatum Tomography, Emission-Computed, Single-Photon Tropanes Female Aged Aged, 80 and over Humans Machine Learning
View on PubMed DOI

Assessing bias in AI-driven psychiatric recommendations: A comparative cross-sectional study of chatbot-classified and CANMAT 2023 guideline for adjunctive therapy in difficult-to-treat depression.

Psychiatry research Jun 1, 2025
The integration of chatbots into psychiatry introduces a novel approach to support clinical decision-making, but biases in their recommendations pose significant concerns. This study investigates potential biases in chatbot-generated recommendations ...
Psychiatry Clinical Decision-Making Cross-Sectional Studies Humans Artificial Intelligence Depressive Disorder, Treatment-Resistant Canada Generative Artificial Intelligence Bias Practice Guidelines as Topic Antidepressive Agents
View on PubMed DOI

Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample.

The Journal of clinical psychiatry Jan 1, 2018
OBJECTIVE: The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome.
Depressive Disorder, Treatment-Resistant Male Treatment Outcome Humans Forecasting Middle Aged Machine Learning Models, Statistical Female Depressive Disorder, Major Antidepressive Agents Adult
View on PubMed DOI
Popular Topics
Recent Journals