Projects

Our research team aims to innovate in the area of methodology for medical sciences including preclinical, clinical and social health sciences. Different projects focus on medical statistics, medical informatics and statistical and modelling algorithms.

running projects

Mapping care pathways in children with TBI using machine learning

V. De Deken & K. Barbé

In general, brain injuries present a significant societal burden. Focus should be brought not only to prevention but also towards improved quality of care. Currently, there is too much unexplainable variation in care trajectories. One category of patients less studied in this domain is pediatric traumatic brain injuries. To overcome this problem, administrative data holds the potential to provide a solution. In this project, we aim to identify different care pathways using machine learning techniques in order to better understand the variation in care and work towards improving the quality of care received by patients.

An artificial intelligent consultant to enhance statistical thinking

P. Savieri, K. Barbé & L. Stas

Statistical consultants frequently encounter researchers seeking support for their study design and data analysis. Such requests for support often encompass doubts in terms of the type of analysis warranted. Although academic researchers obtained specific training in quantitative courses, the lack of hands-on experience often makes researchers feel uncomfortable with respect to statistical choices and their data analyses. In this project, we intend to develop an artificial intelligence (AI) system to provide feedback to the researcher. The tools will be interactive web applications built from the Shiny package within the statistical R environment. The apps will not be exhaustive, but will provide the necessary scientific tutoring, references and reflections for specific statistical techniques/assumptions which are considered healthy thinking approaches.

Radiomics in cancer radiotherapy: unraveling the biology to optimize treatment

A. Rifi & K. Barbé

Radiomics can be used to provide valuable information for personalized cancer therapy. However, the dimensionality implies that radiomics renders a huge data set with many quantitative variables extracted from CT images of the tumor. One way to deal with this dimensionality is by applying random forests as a multivariate classification tool to analyze the radiomics data to predict tumor response. Although such random forests can cope with high dimensional data, its predictions are a black box system. In this project, we will break in into the black box. By dedicated animal trials, we will explore the decision-making process of the random forest to understand what triggers specific predictions.

Random neural network forests decision support for markerless stereotactic body radiotherapy

C. Raets & K. Barbé

In the field of dosimetry, clinicians define the correct dose of radiotherapy to treat cancer patients. In this project, we examine and design the potential of random forests for the dose calculation. By acknowledging that neural networks allow modelling complex relationships; we wish to combine random forests and neural networks such that an ensemble of 'simpler' neural networks, yet with random weights, can be obtained to alleviate potential overfitting leading to more precise dose calculations.

finished projects

Adaptive design in preclinical research

S. Blotwijk & K. Barbé

Several forms of adaptive designs have been developed to optimize experimental designs, to minimize required sample sizes, to guarantee sufficient data, to most efficiently identify valuable research domains, and for many other reasons. Unfortunately, the statistical methodology is usually not suitable for the small sample sizes. We set out to remedy this situation by developing appropriate methodology and making it available through open source software, such that preclinical researchers can immediately apply it in practice.
PhD. Susanne Blotwijk, 2023

PROMoting Informed and Shared decisions about E-Health solutions for older adults with cognitive impairments and their informal caregivers (PROMISE)

S. Dequanter, M. Fobelets & R. Buyl

The number of older adults with cognitive impairments (MCI, dementia) is growing in fast pace. The development of technologies dedicated to health and well-being (e-Health) offers potential to support these older adults and their informal caregivers, but is not always adapted to their needs and preferences. This FWO funded bilateral collaborative Flanders-Québec project supports these groups in making informed and shared decisions about e-Health solutions to improve their health and well-being and to facilitate aging in place. The research project includes qualitative research methods as well as systematic literature reviews and ultimately, the development of an electronic decision support tool.
PhD. Samantha Dequanter, 2022

Fractional-order Wiener-Hammerstein systems for bio-impedance spectroscopy

H. Shaikh & K. Barbé

Wiener-Hammerstein models are a popular class of nonlinear dynamic time series as a simple type of nonlinear Volterra model. The Volterra model holds the same properties as what is observed in linear time series for nonlinear time series. As a result, the nonlinear system is decomposed into a purely linear and purely nonlinear part. In this project, we allow the linear dynamics to show fractional behaviour specifically of interest in the field of (bio)spectroscopy. Moreover, the project aims for algorithmic innovations which speeds up the training of the Wiener-Hammerstein model considerably.
PhD. Hanif Shaikh, 2022

End-to-end errors in the Belgian electronic prescription

S. Van Laere & R. Buyl

The Belgian electronic prescription has found its entry in Belgium in 2007. Since 2013, pilot tests have been set up and in 2014 the Recip-e project has found its entrance to the broad Belgian public. One of the biggest changes came in 2017 in this project by means of a unique Recip-e ID (RID) barcode printed on top of each proof of electronic prescription to facilitate the pharmacist. In this project we investigate possible errors in the workflow between the traditional 3 Ps (prescriber, patient and pharmacist) and the tarification service.
PhD. Sven Van Laere, 2020
Postdoc project, finished 2023

Akritas-Arnold tests to assess nanoParticles to Activate immune Responses against Cancer

O. Olarte & K. Barbé

Contrast tests achieve a high power but unfortunately non-parametric contrast tests are limited to the Jonckheere-Terpstra test. Emerging tests of the Akritas-Arnold type is not fully understood and explored. This family of tests can be positioned within the rank randomization tests. In this project, we aim at a better understanding of the mathematical properties of the Akritas-Arnold tests which will be validated on a preclinical study on immunotherapy against cancer.
Postdoc project, finished 2023