Interpretability is fundamental in healthcare problems and the lack of it in deep learning
models is currently the major barrier in the usage of such powerful algorithms in the field …

Accurate prediction of disease trajectories is critical for early identification and timely
treatment of patients at risk. Conventional methods in survival analysis are often constrained …

A key question in clinical practice is accurate prediction of patient prognosis. To this end,
nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in …

Objectives: This study sought to evaluate the prognostic effect of carbohydrate antigen-125
(CA125)–guided therapy (CA125 strategy) versus standard of care (SOC) after a …

Abstract Objective: The ALSFRS-R is limited by multidimensionality, which originates from
the summation of various subscales. This prevents a direct comparison between patients …

Background Cognitive impairment is common in patients treated with hemodialysis. The
trajectory of cognitive function and risk factors for cognitive decline remain uncertain in this …

Multiple system atrophy (MSA) is a rare neurodegenerative disease, with limited
understanding of disease progression and prognostic factors. We leveraged the data of a …

Longitudinal studies often incur several problems that challenge standard statistical
methods for data analysis. These problems include non-ignorable missing data in …

Background: Identifying predictors of conversion to Alzheimer's disease (AD) is critically
important for AD prevention and targeted treatment. Objective: To compare various clinical …

There is a need for new approaches and endpoints in oncology drug development,
particularly with the advent of immunotherapies and the multiple drug combinations under …