Care Coordination, Lymphoma, Non-Hodgkin
Conditions
Keywords
Artificial intelligence, Non-Hodgkin lymphoma, Care pathway
Brief summary
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.
Detailed description
Non- Hodgkin Lymphomas require rigorous treatment protocols, including intensive intravenous chemotherapy or targeted oral therapies. Secondary immunosuppression necessitates oral anti-infective prophylaxis (such as valacyclovir or Bactrim forte) to prevent opportunistic complications. However, the literature reports figures of up to 50% of patients experiencing adherence difficulties on oral therapies, compromising treatment efficacy, increasing the risk of severe infections, prolonged hospitalizations, and consequently, additional costs for the healthcare system. This project proposes to develop an innovative artificial intelligence (AI) tool, based on real-world data, to detect early signs of non-adherence and enable targeted intervention by healthcare teams. Our approach combines analysis of clinical data (patient, disease, dispensing history, laboratory results, drug interactions) and machine learning algorithms (supervised machine learning and neural networks) to identify at-risk profiles. The tool will generate a real-time alert and offer the patient's referring physician and coordinating nurse tailored recommendations, such as an automated reminder, a dedicated nursing consultation, etc. An intuitive interface will allow clinicians and nurses to visualize compliance trends and act quickly. This project relies on a multidisciplinary team (hematologists, advanced practice nurses (APNs), data scientists, AI experts) and patient partners to validate the tool in real-world conditions.
Interventions
OTHERRetrospective Group
For the retrospective group of 20 patients.
OTHERProspective Group
Follow-up of the patients for the prospective group
Sponsors
Grand Hôpital de Charleroi
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| ROC-AUC | 2027 | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| F1-score | When the data will be avalaible, at the end of 2027 | 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. |
Countries
Belgium
Contacts
CONTACTMarie Detrait, MD, PhD
CONTACTAline Gillain, MedSciences
PRINCIPAL_INVESTIGATORMarie Detrait, MD, PhD
Grand Hôpital de Charleroi
Outcome results
None listed