BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to play a role in its pathophysiology. Peripheral inflammatory biomarkers, such as the neutrophil-to-lymph...
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...
Studies in health technology and informatics
Aug 7, 2025
The high prevalence of failed antidepressant deprescription attempts makes it difficult for clinicians to identify suitable candidates for discontinuation. In this study, we use the Pharmaceutical Benefits Scheme (PBS) dataset, which contains rich lo...
BACKGROUND: The combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic features ('psychotic depression'). The present study aims to identify sociodemographic and clinical pred...
Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary...
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 ...
OBJECTIVES: To demonstrate an innovative method combining machine learning with comparative effectiveness research techniques and to investigate a hitherto unstudied question about the effectiveness of common prescribing patterns.
Non-specific response to treatment (NSRT) is the primary contributor to the failure of randomized clinical trials in major depressive disorder (MDD). The objective of this study is to develop artificial neural network (ANN) models to predict the indi...
Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to develop antidepressant medications appropriate for outperforming placebo. Treatment effect in randomized,...
Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that pr...
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