Sofie Van Hoecke

Ghent University | UGhent · Department of Electronics and Information Systems

Feature selection is a crucial step in developing robust and powerful machine learning models. Feature selection techniques can be divided into two categories: filter and wrapper methods. While wrapper methods commonly result in strong predictive performances, they suffer from a large computational complexity and therefore take a significant amount...

Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improve-ments, often at the expense of requiring more data and more expensive training procedures. Despite all these efforts and their satisfactory...

A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate...

The use of speech as a digital biomarker to detect stress levels is increasingly gaining attention. Yet, heterogeneous effects of stress on specific acoustic speech features have been observed, possibly due to previous studies’ use of different stress labels/categories and the lack of solid stress induction paradigms or validation of experienced st...

The World Health Organization (WHO) recently published target product profiles (TPPs) for neglected tropical diseases (NTDs) to inform and accelerate the development of diagnostics tools necessary to achieve targets in the decade ahead. These TPPs describe the minimal and ideal requirements for various diagnostic needs related to NTD specific use-c...

In this paper, a hybrid leak localization approach in WDNs is proposed, combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning-based leak localization model. A key element of the methodology is that discrepancies between simulated and mea...

Background The diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation. Therefore...

A paper of Alsinglawi et al was recently accepted and published in Scientific Reports. In this paper, the authors aim to predict length of stay (LOS), discretized into either long (> 7 days) or short stays (< 7 days), of lung cancer patients in an ICU department using various machine learning techniques. The authors claim to achieve perfect results...

Background Insomnia, eating disorders, heart problems and even strokes are just some of the illnesses that reveal the negative impact of stress overload on health and well-being. Early detection of stress is therefore of utmost importance. Whereas the gold-standard for detecting stress is by means of questionnaires, more recent work uses wearable s...

As companies rely on an ever increasing number of connected devices for their day to day operations, a need arises for automated anomaly detectors to constantly observe crucial device metrics in real time to prevent downtime and data loss. As production environments tend to monitor a huge amount of these metrics, it prevents current state-of-the-ar...

Background Beta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling is the golden standard to predict drug concentrations. However, currently available PopPK models often lack predictive accuracy, making them less suited to guide dosing regimen adaptations. Furthermor...

Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, often at the expense of requiring more data and more expensive training procedures. Despite all these efforts and their satisfactory p...

Feature selection is a crucial step in developing robust and powerful machine learning models. Feature selection techniques can be divided into two categories: filter and wrapper methods. While wrapper methods commonly result in strong predictive performances, they suffer from a large computational complexity and therefore take a significant amount...

Background With the World Health Organization’s (WHO) publication of the 2021–2030 neglected tropical diseases (NTDs) roadmap, the current gap in global diagnostics became painfully apparent. Improving existing diagnostic standards with state-of-the-art technology and artificial intelligence has the potential to close this gap. Methodology/Princip...

Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To realize effective time series visualization, four requirements have to be met; a tool should be (1)...

The use of speech as a digital biomarker to detect stress levels is increasingly gaining attention. Yet, heterogeneous effects of stress on specific acoustic speech features have been observed, possibly due to previous studies’ use of different stress labels/categories and the lack of solid stress induction paradigms or validation of experienced st...

The inception of Relational Graph Convolutional Networks (R-GCNs) marked a milestone in the Semantic Web domain as it allows for end-to-end training of machine learning models that operate on Knowledge Graphs (KGs). R-GCNs generate a representation for a node of interest by repeatedly aggregating parametrised, relation-specific transformations of i...

The high functional performance exhibited by modern applications is very often established by an aggregation of various intricate mechanical mechanisms, providing the required motion dynamics to the overall system. Above all, the mechanism's behavior should be reliable for a wide range of operating conditions to assure at all times appropriate func...

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/...

Remaining useful life is of great value in the industry and is a key component of Prognostics and Health Management (PHM) in the context of the Predictive Maintenance (PdM) strategy. Accurate estimation of the remaining useful life (RUL) is helpful for optimizing maintenance schedules, obtaining insights into the component degradation, and avoiding...

Annealing and galvanization production lines in steel mills run continuously to maximize production throughput. As a part of this process, individual steel coils are joined end-to-end using mash seam welding. Weld breaks result in a production loss of multiple days, so non-destructive, data-driven techniques are used to detect and replace poor qual...

Time series processing and feature extraction are crucial and time-intensive steps in conventional machine learning pipelines. Existing packages are limited in their applicability, as they cannot cope with irregularly-sampled or asynchronous data and make strong assumptions about the data format. Moreover, these packages do not focus on execution s...

Since artificial intelligence (AI) and, more specifically, machine learning have found their way into medical research, expectation for these techniques to advance patient care has been high. These hopes are also present in the infection management field, where an ongoing need exists to ameliorate antimicrobial usage for the benefit of the patient...

Traditionally, neural networks are viewed from the perspective of connected neuron layers represented as matrix multiplications. We propose to compose these weight matrices from a set of orthogonal basis matrices by approaching them as elements of the real matrices vector space under addition and multiplication. Making use of the Kronecker product...

Companies are increasingly gathering and analyzing time-series data, driven by the rising number of IoT devices. Many works in literature describe analysis systems built using either data-driven or semantic (knowledge-driven) techniques. However, little to no works describe hybrid combinations of these two. Dyversify, a collaborative project betwee...

Time series processing and feature extraction are crucial and time-intensive steps in conventional machine learning pipelines. Existing packages are limited in their real-world applicability, as they cannot cope with irregularly-sampled and asynchronous data. We therefore present $\texttt{tsflex}$, a domain-independent, flexible, and sequence first...

Manufacturers can plan predictive maintenance by remotely monitoring their assets. However, to extract the necessary insights from monitoring data, they often lack sufficiently large datasets that are labeled by human experts. We suggest combining knowledge-driven and unsupervised data-driven approaches to tackle this issue. Additionally, we presen...

Cam-follower mechanisms are key in various mechatronic applications to convert rotary to linear reciprocating motions. The dynamic behavior of these systems relies on the design parameters such as the cam shape and follower mass. It appears that for some combinations of system parameters, continuous contact between the cam and follower cannot be as...

Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in th...

Solenoid valves are essential components of industrial systems and therefore widely used. As they suffer from high failure rates in the field, fault prognosis of these assets plays a major role for improving their maintenance and reliability. In this work, Bayesian convolutional neural networks are used to predict the remaining useful life (RUL) of...

Anomalies and faults can be detected, and their causes verified, using both data-driven and knowledge-driven techniques. Data-driven techniques can adapt their internal functioning based on the raw input data but fail to explain the manifestation of any detection. Knowledge-driven techniques inherently deliver the cause of the faults that were dete...

Dynamic models of mechatronic systems are abundantly used in the context of motion control and design of complex servo applications. In practice, these systems are often plagued by unknown interactions, which make the physics-based relations of the system dynamics only partially known. This article presents a neural network augmented physics (NNAP)...

Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or preterm using a public resource...

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19...

The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance req...

This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical not...

Solenoid valves are critical components in many process control systems, as their failure is often a root cause for plant shutdowns. Therefore, the ability to predict the remaining useful life (RUL) of solenoid valves is highly desirable. In this paper, a novel data-driven RUL prediction methodology for solenoid valves is proposed, by training deep...

The Matrix Profile is a state-of-the-art time series analysis technique that can be used for motif discovery, anomaly detection, segmentation and others, in various domains such as healthcare, robotics, and audio. Where recent techniques use the Matrix Profile as a preprocessing or modeling step, we believe there is unexplored potential in generali...

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to...

Companies are increasingly measuring their products and services, resulting in a rising amount of available time series data, making techniques to extract usable information needed. One state-of-the-art technique for time series is the Matrix Profile, which has been used for various applications including motif/discord discovery, visualizations and...

Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or preterm using a public resource...

Communication networks are complex systems consisting of many components each producing a multitude of system metrics that can be monitored in real-time. Anomaly Detection (AD) allows to detect deviant behavior in these system metrics. However, in communication networks, large amounts of domain knowledge and huge manual efforts are required to effi...

Mechatronic systems are plagued by nonlinearities and contain uncertainties due to, amongst others, interactions with their environment. Models exhibiting accurate multistep predictive capabilities can be valuable in the context of motion control and design of servo controlled systems. Neural Network Augmented Physics (NNAP) models are presented in...

A key barrier in the industrial adoption of condition monitoring is the lack of large and reliable data sets about the full lifetime of bearings in machines. This data is useful for model training as well as for validation purposes. This paper demonstrates how a living lab, consisting of 7 identical drive train subsystems , can enable smart machine...

Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL),...

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset,...

Preterm birth is the leading cause of death among children under five years old. The pathophysiology and etiology of preterm labor are not yet fully understood. This causes a large number of unnecessary hospitalizations due to high–sensitivity clinical policies, which has a significant psychological and economic impact. In this study, we present a...

This paper focuses on the development of a physics-based diagnostic tool for alternating current (AC) solenoid valves which are categorized as critical components of many machines used in the process industry. Signal processing and machine learning based approaches have been proposed in the literature to diagnose the health state of solenoid valves...

Abstract Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects’ demographics, baseline psychological information, and five consecutiv...

Glaucoma is a major eye disease, leading to vision loss in the absence of proper medical treatment. Current diagnosis of glaucoma is performed by ophthalmologists who are often analyzing several types of medical images generated by different types of medical equipment. Capturing and analyzing these medical images is labor-intensive and expensive. I...

Background Headache disorders are an important health burden, having a large health-economic impact worldwide. Current treatment & follow-up processes are often archaic, creating opportunities for computer-aided and decision support systems to increase their efficiency. Existing systems are mostly completely data-driven, and the underlying models a...

Bone age is an essential measure of skeletal maturity in children with growth disorders. It is typically assessed by a trained physician using radiographs of the hand and a reference model. However, it has been described that the reference models leave room for interpretation leading to a large inter-observer and intra-observer variation. In this w...

Assessing upfront the causes and effects of failures is an important aspect of system manufacturing. Nowadays, these analyses are performed by a large number of experts. To enable semantic unification and easy operationalization of these risk analyses, this paper demonstrates an approach to automatically map the captured information into an ontolog...

Sensors, inside internet-connected devices, analyse the environment and monitor possible unwanted behaviour or the malfunctioning of the system. Current risk analysis tools, such as Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA), provide prior information on these faults together expert-driven insights of the system. Many peo...

In order to minimize operation and maintenance costs and extend the lifetime of rotating machinery, damaging conditions and faults should be detected early on and automatically. To enable this, sensor streams should continuously be monitored, processed and interpreted. In recent years, infrared thermal imaging has gained attention for said purpose....

An important challenge in home automation is the energy efficient optimization of the indoor environment. This relies on the solution of a multi-objective optimization problem where energy efficiency and comfort parameters are maximized simultaneously. This paper presents three data-driven control algorithms based on machine learning techniques, wh...

This paper describes the items, scale validity and scale reliability of a self-report questionnaire that measures bystander behavior in cyberbullying incidents among adolescents, and its behavioral determinants. Determinants included behavioral intention, behavioral attitudes, moral disengagement attitudes, outcome expectations, self-efficacy, subj...