Publications
Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep : large-scale validation using machine learning Ghent University
Assessing Upper Limb Function in Breast Cancer Survivors Using Wearable Sensors and Machine Learning in a Free-Living Environment Vrije Universiteit Brussel University of Antwerp KU Leuven
(1) Background: Being able to objectively assess upper limb (UL) dysfunction in breast cancer survivors (BCS) is an emerging issue. This study aims to determine the accuracy of a pre-trained lab-based machine learning model (MLM) to distinguish functional from non-functional arm movements in a home situation in BCS. (2) Methods: Participants performed four daily life activities while wearing two wrist accelerometers and being video recorded. ...
Machine Learning Techniques Outperform Conventional Statistical Methods in the Prediction of High Risk QTc Prolongation Related to a Drug-Drug Interaction Vrije Universiteit Brussel
In clinical practice, many drug therapies are associated with prolongation of the QT interval. In literature, estimation of the risk of prescribing drug-induced QT prolongation is mainly executed by means of logistic regression; only one paper reported the use of machine learning techniques. In this paper, we compare the performance of both techniques on the same dataset. High risk for QT prolongation was defined as having a corrected QT ...
Can machine learning models predict maternal and newborn healthcare providers' perception of safety during the COVID-19 pandemic? A cross-sectional study of a global online survey Institute of Tropical Medicine
Background Maternal and newborn healthcare providers are essential professional groups vulnerable to physical and psychological risks associated with the COVID-19 pandemic. This study uses machine learning algorithms to create a predictive tool for maternal and newborn healthcare providers' perception of being safe in the workplace globally during the pandemic. Methods We used data collected between 24 March and 5 July 2020 through a global ...
Automatic wheat ear counting using machine learning based on RGB UAV imagery KU Leuven Ghent University
Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass Vrije Universiteit Brussel
BACKGROUND: This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors.
METHODS: Four ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, ...
Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation Hasselt University KU Leuven
Detecting Code Smells using Machine Learning Techniques: Are We There Yet? Vrije Universiteit Brussel
During the last decades several code smell detection tools have been proposed.
However, the literature shows that the results of these tools can be subjective and are intrinsically tied to the nature and approach of the detection.
In a recent work the use of Machine-Learning (ML) techniques for code smell ...