BACKGROUND: Whether machine learning approaches are superior to classical statistical models for survival analyses, especially in the case of lack of proportionality, is unknown.
Early mortality after hemodialysis (HD) initiation significantly impacts the longevity of HD patients. This study aimed to quantify the effect sizes of risk factors on mortality using various machine learning approaches. A cohort of 3284 HD patients ...
Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challen...
With an increasing focus on precision medicine in medical research, numerous studies have been conducted in recent years to clarify the relationship between treatment effects and patient characteristics. The treatment effects for patients with differ...
BACKGROUND: Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the mos...
In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a terminal event, such as death. Therefore, jointly modeling longitudinal measurements, recurrent events...
BACKGROUND: Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive and fatal prion disease with significant public health implications. Survival is heterogenous, posing challenges for prognostication and care planning. We developed a surv...
Breast cancer is one of the most prevalent cancers with an increasing trend in both incidence and mortality rates in Iran. Survival analysis is a pivotal measure in setting appropriate care plans. To the best of our knowledge, this study is pioneeri...
Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and enormous quantities of medical data by i...