Mortality risk modeling and forecasting is one of the key tasks of social security institutions and insurance companies. Traditionally used stochastic mortality models, such as the Lee-Carter model, require fulfillment of formal assumptions that cannot always be met in real-life scenarios (e.g. time independence of age-specific improvement rates). Alternative approaches are based on deep...
Stroke can lead to a wide range of symptoms including acute motor impairment, post-stroke depression or cognitive impairment. Anticipating these outcomes early on and understanding their underlying causes would enable clinicians to initiate appropriate targeted treatments, which could not only help reduce the severity of symptoms but also improve long-term recovery. Convolutional neural...
This presentation explores the statistical challenges and comparative performance of various deep learning models for the automated detection and classification of neurological diseases from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans. Building upon initial findings that demonstrated the potential of Convolutional Neural Networks (CNNs) in recognizing rare brain...
Random forest is a widely used machine learning method across the life sciences due to its high predictive performance, minimal assumptions, and flexibility in handling diverse data types. However, a critical yet often overlooked property of random forest is its inherent non-determinism: repeated runs on the same data set can produce different prediction models. This variability can compromise...
Statistical prediction models for binary outcomes are becoming increasingly popular. One signifi‐
cant challenge is calibrating these models to suit the characteristics of a target population that is
structurally different from the original population. Calibration is especially challenging when there
is no training data available from the target population. To address this problem, we...
