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Towards a Transportable Causal Network Model Based on Observational Healthcare Data
Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare.
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available.
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task.
A Survey on Causal Discovery: Theory and Practice
Understanding the laws that govern a phenomenon is the core of scientific progress.
ACTA: A Mobile-Health Solution for Integrated Nudge-Neurofeedback Training for Senior Citizens
Beyond SENIOR, ACTA represents a highly-usable, accessible, low-cost, new-generation mobile-health solution to promote independent aging and effective motor-cognitive training support, while empowering the elderly in their own aging.
Comparison of Attention-based Deep Learning Models for EEG Classification
Conclusions: with this work, we shed light over the role of different attention mechanisms in the classification of normal and abnormal EEG patterns.
