Over the past decades, there has been considerable interest in applying statistical machine
learning methods in survival analysis. Ensemble based approaches, especially random …

Prognostic modelling is important in clinical practice and epidemiology for patient
management and research. Electronic health records (EHR) provide large quantities of data …

Identification of different risk factors and early prediction of mortality for patients with heart
failure are crucial for guiding clinical decision-making in Intensive care unit cohorts. In this …

Background Despite considerable research efforts during the last five decades, the
prediction of suicidal behaviour (SB) using traditional model-based statistical has been …

Salivary gland malignancies are rare and are often acompanied by poor prognoses. So,
identifying the populations with risk factors and timely intervention to avoid disease …

A significant fraction of data in cloud storage is rarely accessed, referred to as cold data.
Accurately identifying and efficiently managing cold data on cost-effective storages is one of …

Background/objectives There is currently no widely accepted approach to identify patients at
increased risk for sporadic pancreatic cancer (PC). We aimed to compare the performance …

Objective: To explore the application of the Cox model based on extreme learning machine
in the survival analysis of patients with chronic heart failure. Methods: The medical records …

Background Clinical risk assessment with quantitative formal risk scores may add to intuitive
physician risk assessment and are advised by the international guidelines for the …

Current survival analysis of cancers confronts two key issues. While comprehensive
perspectives provided by data from multiple modalities often promote the performance of …