Clinical trial · Observational
AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients.
AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients, Integrating the Complete Care Pathway and an Interoperable Clinical Interface With Algorithms Paired With Explainability Tools.
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Care Coordination | — | UNRESOLVED | — |
| Lymphoma, Non-Hodgkin | Non-Hodgkin Lymphoma | ONTOLOGY_EXACT | 0.90 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Prospective Group | Other | — | UNRESOLVED |
| Retrospective Group | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Retrospective cohort
- description
- A retrospective cohort from 2019 to 2024 comprising 350 lymphoma patients who were monitored on an empirical basis.
- interventionNames
- Other: Retrospective Group
- label
- Prospective cohort
- description
- A prospective cohort study involving up to 210 consecutive patients, starting in November 2025, with the aim of developing a decision-support tool using machine learning.
- interventionNames
- Other: Prospective Group
Primary outcomes (1)
- measure
- ROC-AUC
- timeFrame
- 2027
- description
- 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
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * 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 Exclusion Criteria: * All other patients who did not meet the eligibility criteria
References
Publications (0)
Data not yet available