A5034724960- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Fault Detection and Control Systems
- Anomaly Detection Techniques and Applications
- Mental Health via Writing
- Gene expression and cancer classification
- Machine Learning in Healthcare
- Health Literacy and Information Accessibility
- Machine Fault Diagnosis Techniques
- Digital Mental Health Interventions
Centre Hospitalier Universitaire de Liège
2023-2024
Autonomous University of Queretaro
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....
The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source insights into patient experiences. Traditional patient-reported experience outcomes measures (PREMs/PROMs) often fall short addressing the complexities these experiences due to subjectivity their inability precisely target questions asked. In contrast, diary recordings offer promising solution. They can provide comprehensive picture psychological well-being, encompassing both...
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...
The detection of unexpected events represents, currently, one the most critical challenges dealing with electromechanical system diagnosis. In this regard, machine learning based algorithms widely applied in other fields application are being considered now to face novelty during electric monitoring. study, an electrical monitoring scheme is for performance evaluation, where vibration signals under different bearing fault conditions acquired. Thus, common framework, that is, a set features...