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NCT Number: NCT07546188

AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients.

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.

Recruiting

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Key information

Conditions

Lymphoma, Non-Hodgkin Care Coordination Hemic and Lymphatic Diseases Immune System Diseases Immunoproliferative Disorders Lymphatic Diseases Lymphoma Lymphoproliferative Disorders Neoplasms Neoplasms by Histologic Type

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Grand Hôpital de Charleroi

Charleroi, Hainaut, 6060, Belgium

Location status: Recruiting

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Retrospective Group

Other

For the retrospective group of 20 patients.

Prospective Group

Other

Follow-up of the patients for the prospective group

Primary outcomes

  1. ROC-AUC

    Time frame: 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 outcomes

  1. F1-score

    Time frame: 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.

Other outcomes

  1. Recall for the positive class

    Time frame: 2027

    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.

Interested in participating?

Recruiting

Interested in participating?

Request Info

Sponsors and collaborators

Lead sponsor

Grand Hôpital de Charleroi

Other

Registry information

Official study title

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.

Acronym: LNH-AI-Tools

Important dates

Study start
2026
Primary completion
2027
Study completion
2029
First posted
Apr 22, 2026
Registry last updated
Apr 22, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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