This research forms part of a continuous quality improvement initiative. It aims to assess patient compliance of oral therapies by artificial intelligence. It could overcome the limitations of current practices and enhance the responsiveness and accuracy of clinical interventions.
All patients aged 18 and over who are treated in the Haematology Department at the Grand Hôpital de Charleroi from November 2025 onwards
Treated for a lymphoma, Non Hodgkin
Capable of giving informed consent
All other patients who did not meet the eligibility criteria
Patients treated in the Haematology Department at Charleroi General Hospital for Non-Hodgkin lymphoma.
Cohort
Prospective
Retrospective Group
Other interventionFor the retrospective group of 20 patients.
Prospective Group
Other interventionFollow-up of the patients for the prospective group
ROC-AUC
Description: ROC-AUC : Receiver Operating Characteristic - Area Under the Curve is a performance metric for binary classification prediction algorithms. ROC Curve: Plots the True Positive Rate (sensitivity) against the False Positive Rate (1-specificity) at various classification thresholds. AUC: The area under this curve (ranging from 0 to 1). A higher AUC indicates better model performance-1.0 is perfect, 0.5 is random guessing. ROC-AUC evaluates how well the model distinguishes between classes, regardless of the classification threshold. Time Frame: When the data will be avalaible, at the end of 2027
F1-score
F1-Score is a performance metric for classification algorithms, the harmonic mean of Precision (correct positive predictions / total positive predictions) and Recall (correct positive predictions / actual positives). Formula: F1 = 2 × (Precision × Recall) / (Precision + Recall) Range: 0 to 1, where 1 is perfect precision and recall, and 0 is the worst. F1-Score balances precision and recall, making it ideal when you need to avoid both false positives and false negatives.
Recall for the positive class
Recall for the positive Class is a metric for binary classification that answers: "What proportion of actual positives was correctly identified by the model?" Formula: Recall = True Positives / (True Positives + False Negatives) Range: 0 to 1, where 1 means all positives were correctly predicted, and 0 means none were. High recall means the model is good at capturing most positive cases, but it may also include more false positives. It's critical when missing a positive (false negative) is costly.
Click on the lead sponsor to view all of their trials.
