Publicaties
Over-accrual in Bayesian adaptive trials with continuous futility stopping Universiteit Hasselt
Background: We explore frequentist operating characteristics of a Bayesian adaptive design that allows continuous early stopping for futility. In particular, we focus on the power versus sample size relationship when more patients are accrued than originally planned. Methods: We consider the case of a phase II single-arm study and a Bayesian phase II outcome-adaptive randomization design. For the former, analytical calculations are possible; for ...
High-dimensional data Universiteit Hasselt
ig data is a relatively recent term that has emerged because of the rapid collection and generation of data, the increase in storage and computing capacity, and the rise of a new generation of machine learning algorithms. It is generally characterized by 3 "V's." The first "V," volume, refers to the size and scale of the data. This can be the number of subjects (observations) or variables (covariates, features) in the dataset. Velocity, the ...
Generalized linear models Universiteit Hasselt
M achine learning (ML) algorithms use statistical models to find patterns or structures in data. These models can formulate predictions for new observations on the basis of these patterns, which the ML algorithm can translate into decisions. The simple linear regression model 1 is the most fundamental of all statistical and ML models. It is used to describe an effect of a continuous explanatory variable (covariate) on the mean (expected) value ...
Support vector machines Universiteit Hasselt
A support vector machine (SVM) is a supervised machine learning (ML) method capable of learning from data and making decisions. The fundamental principles of the SVM were already introduced in the 1960s by Vapnik and Chervonenkis 1 in a theory that was further developed throughout the next decennia. However, it was only in the 1990s that SVMs attracted greater attention from the scientific community , and this was attributed to 2 significant ...
Minimization in randomized clinical trials Universiteit Hasselt
In randomized trials, comparability of the treatment groups is ensured through allocation of treatments using a mechanism that involves some random element, thus controlling for confounding of the treatment effect. Completely random allocation ensures comparability between the treatment groups for all known and unknown prognostic factors. For a specific trial, however, imbalances in prognostic factors among the treatment groups may occur. ...
Decision trees and random forests Universiteit Hasselt
Cost volume is widely used to establish correspondences in optical flow estimation. However, when dealing with low-texture and occluded areas, it is difficult to estimate the cost volume correctly. Therefore, we propose a replacement: feature correlation transformer (FCTR), a transformer with self-and cross-attention alternations for obtaining global receptive fields and positional embedding for establishing correspondences. With global context ...
Validation of machine learning algorithms Universiteit Hasselt
Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data Universiteit Hasselt
Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change ...
Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution Universiteit Hasselt
The observed isotope distribution is an important attribute for the identification of peptides and proteins in mass spectrometry-based proteomics. Sulphur atoms have a very distinctive elemental isotope definition and therefore, the presence of Sulphur atoms has a substantial effect on the isotope distribution of biomolecules. Therefore, knowledge on the number of Sulphur atoms can improve identification of peptides and proteins.