AIMC Topic: Sertraline

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Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression.

Molecular psychiatry
Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD's complex and varied neuropathology. Identifying biomarkers for antidepressant treatm...

Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Predicting an individual's response to antidepressant medication remains one of the most challenging tasks in the treatment of major depressive disorder (MDD). Our objective was to use the large EMBARC study database to develop an electroe...

Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study.

Expert review of clinical pharmacology
BACKGROUND: Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intellig...

Machine Learning Model for Predicting Sertraline-like Activities and Its Impact on Cancer Chemosensitization.

ACS chemical neuroscience
Selective serotonin reuptake inhibitors (SSRIs) like sertraline are crucial in treating depression and anxiety disorders, and studies indicate their potential as chemosensitizers in cancer therapy. This research develops a machine-learning predictive...