A5043576023- AI in cancer detection
- Context-Aware Activity Recognition Systems
- Wireless Body Area Networks
- Non-Invasive Vital Sign Monitoring
- IoT and Edge/Fog Computing
- Radiomics and Machine Learning in Medical Imaging
- Privacy-Preserving Technologies in Data
- Machine Learning in Healthcare
- Healthcare Technology and Patient Monitoring
- Diabetes Management and Education
- Privacy, Security, and Data Protection
- Digital Mental Health Interventions
- Bioinformatics and Genomic Networks
- Chronic Disease Management Strategies
- Cancer Genomics and Diagnostics
- Gene expression and cancer classification
- Vehicular Ad Hoc Networks (VANETs)
- Advanced Radiotherapy Techniques
- ECG Monitoring and Analysis
- Cancer survivorship and care
- Healthcare Operations and Scheduling Optimization
- COVID-19 and Mental Health
- Internet Traffic Analysis and Secure E-voting
- COVID-19 diagnosis using AI
- Hemodynamic Monitoring and Therapy
Centre Hospitalier Universitaire de Liège
2019-2024
University of Liège
2024
IMEC
2019
KU Leuven
2019
Recurrence is a critical aspect of breast cancer (BC) that inexorably tied to mortality. Reuse healthcare data through Machine Learning (ML) algorithms offers great opportunities improve the stratification patients at risk recurrence. We hypothesized combining features from structured and unstructured sources would provide better prediction results for 5-year recurrence than either source alone. collected preprocessed clinical cohort BC patients, resulting in 823 valid subjects analysis....
Cancer survivors face numerous challenges, and digital health interventions can empower them by enhancing self-efficacy patient activation. This prospective study aimed to assess the impact of an mHealth app on activation in 166 breast colorectal cancer survivors. Participants received a smart-bracelet used access personalized care plans. Data was collected at baseline follow-ups, including patient-reported outcomes clinician feedback. The demonstrated positive impacts overall trial...
Personalized support and assistance are essential for cancer survivors, given the physical psychological consequences they have to suffer after all treatments conditions associated with this illness. Digital assistive technologies proved be effective in enhancing quality of life instance, through exercise monitoring recommendation or emotional prediction. To maximize efficacy these techniques, it is challenging develop accurate models patient trajectories, which typically fed information...
In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and time-series prediction. The collected monitored are heart rate, blood pressure, respiration oxygen saturation heterogeneous patient population in cardiology, postsurgical, dialysis wards. Two aspects elaborated study. first is the high-rate (every minute) statistical...
This study introduces machine learning predictive models to predict the future values of monitored vital signs COVID-19 ICU patients. The main sign predictors include heart rate, respiration and oxygen saturation. We investigated performances developed by considering different approaches. first model was following signs: blood pressure (systolic, diastolic mean arterial, pulse pressure), Similar approach, second using same signs, but it trained tested based on a leave-one-subject-out...
In this work, we present a new device to monitor the five main vital parameters of hospitalized patients: heart rate (HR), respiratory (RR), blood oxygen saturation, pressure (BP), and temperature. The consists one single unit placed on chest with two electrodes connected patient. continuously acquires electrocardiogram (ECG), three-wavelength photoplethysmogram (PPG), bioimpedance, body temperature, three-axis acceleration. These raw data are securely sent via Wi-Fi access point local...
<sec> <title>BACKGROUND</title> Cancer survivors face various challenges but also demonstrate resilience and find ways to adapt cope with life after cancer. Self-efficacy patient activation are two crucial factors that significantly impact the well-being of cancer survivors. These concepts play a vital role in enabling take control their health, manage treatment effectively, achieve positive long-term outcomes. </sec> <title>OBJECTIVE</title> The aim this study is assess mobile health system...
In order to reduce the workload of hospital staff and provide better services hospitalized patients, attempts are made integrate patient monitoring systems directly into networks. Monitoring must respond more technological challenges. They ideally portable wireless, free from bed. At same time, enable follow-up, a large amount information needs be transmitted processed in real time. Challenges design such include energy-efficient processing communication, guaranteeing security measured data....
Accurate and early prediction of breast cancer recurrence is crucial to guide medical decisions treatment success. Machine learning (ML) has shown promise in this domain. However, its effectiveness critically depends on proper hyperparameter setting, a step that not always performed systematically the development ML models. In study, we aimed highlight impact process final performance models through real-world case study by predicting five-year patients. We compared five algorithms (Logistic...
This paper describes an important first step in the development of a custom wearable health platform that allows end-to-end secure monitoring six vital parameters. We explore impact wireless network protocols and security schemes on energy consumption device. The results show efficiency is comparable to existing systems support far less sensor data compromise security.
This paper is an overview of the EU-funded project TRUMPET (https://trumpetproject.eu/), and gives outline its scope main technical aspects objectives. In recent years, Federated Learning has emerged as a revolutionary privacy-enhancing technology. However, further research cast shadow doubt on strength for privacy protection. The goal to develop novel enhancement methods Learning, deliver highly scalable AI service platform researchers, that will enable AI-powered studies siloed,...