AI Medical Compendium Journal:
Journal of psychosomatic research

Showing 1 to 5 of 5 articles

Machine learning for the early prediction of long-term cognitive outcome in autoimmune encephalitis.

Journal of psychosomatic research
BACKGROUND AND OBJECTIVE: Autoimmune encephalitis (AE) is an immune-mediated disease. Some patients experience persistent cognitive deficits despite receiving immunotherapy. We aimed to develop a prediction model for long-term cognitive outcomes in p...

Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.

Journal of psychosomatic research
PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depression on cardiovascular disease (CVD) mortality among hypertensive patients. A key objective is to eluc...

Acute psychological stress-induced progenitor cell mobilization and cardiovascular events.

Journal of psychosomatic research
OBJECTIVE: Certain brain activation responses to psychological stress are associated with worse outcomes in CVD patients. We hypothesized that elevated acute psychological stress, manifesting as greater activity within neural centers for emotional re...

Artificial neural network and decision tree models of post-stroke depression at 3 months after stroke in patients with BMI ≥ 24.

Journal of psychosomatic research
OBJECTIVE: Previous studies have shown that excess weight (including obesity and overweight) can increase the risk of cardiovascular, cerebrovascular and other diseases, but there is no study on the incidence of post-stroke depression (PSD) and relat...

Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease.

Journal of psychosomatic research
OBJECTIVE: Individuals with immune-mediated inflammatory disease (IMID) have a higher prevalence of psychiatric disorders than the general population. We utilized machine-learning to identify patient-reported outcome measures (PROMs) that accurately ...