Predictive Modeling of Long-Term Survivors with Stage IV Breast Cancer Using the SEER-Medicare Dataset.

Importance: Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. Here, we develop a model for identifying individual circumstances where appropriate therapy will extend survival while minimizing adverse events.

Objective: Our goal is to develop high-confidence deep learning (DL) models to predict survival in individual stage IV breast cancer patients based on their unique circumstances generated by patient, cancer, treatment, and adverse event variables.

AI Survival Prediction Modeling: The Importance of Considering Treatments and Changes in Health Status over Time.

Background And Objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis.

Cost of cardiovascular disease events in patients with and without type 2 diabetes and factors influencing cost: a retrospective cohort study.

Background: Cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are non-communicable diseases that impose a significant economic burden on healthcare systems, particularly in low- and middle-income countries. The purpose of this study was to evaluate the hospital treatment cost for cardiovascular disease events (CVDEs) in patients with and without diabetes and identify factors influencing cost.

Method: We conducted a retrospective, cross-sectional study using administrative data from three public tertiary hospitals in Malaysia.

Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction.

Background: Simultaneous multi-slice (SMS) bSSFP imaging enables stress myocardial perfusion imaging with high spatial resolution and increased spatial coverage. Standard parallel imaging techniques (e.g.