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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Retrospective cohort | A retrospective cohort from 2019 to 2024 comprising 350 lymphoma patients who were monitored on an empirical basis. |
| |
| Prospective cohort | 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Retrospective Group | Other | For the retrospective group of 20 patients. |
| |
| Measure | Description | Time Frame |
|---|---|---|
| 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 | 2027 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
Inclusion Criteria:
Exclusion Criteria:
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Patients treated in the Haematology Department at Charleroi General Hospital for Non-Hodgkin lymphoma.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Marie Detrait, MD, PhD | Contact | 0032 60 11 20 08 | marie.detrait@ghdc.be | |
| Aline Gillain, MedSciences | Contact | 0032 60 11 00 89 | aline.gillain@ghdc.be |
| Name | Affiliation | Role |
|---|---|---|
| Marie Detrait, MD, PhD | Grand Hôpital de Charleroi | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Grand Hôpital de Charleroi | Recruiting | Charleroi | Hainaut | 6060 | Belgium |
At present, this research is being carried out in-house; following analysis, this option could be considered if the model can be adapted for use elsewhere.
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| Prospective Group |
| Other |
Follow-up of the patients for the prospective group |
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| When the data will be avalaible, at the end of 2027 |
| 2027 |
| ID | Term |
|---|---|
| D008228 | Lymphoma, Non-Hodgkin |
| ID | Term |
|---|---|
| D008223 | Lymphoma |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D008232 | Lymphoproliferative Disorders |
| D008206 | Lymphatic Diseases |
| D006425 | Hemic and Lymphatic Diseases |
| D007160 | Immunoproliferative Disorders |
| D007154 | Immune System Diseases |
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