Идёт набор NCT07546188

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

— Наблюдательное Lymphoma, Non-Hodgkin Care Coordination

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают: В протоколе указаны: Retrospective Group, Prospective Group.
Кому может быть актуально: Состояния в реестре: Lymphoma, Non-Hodgkin, Care Coordination. Базовые параметры: от 18 лет · Все.
Что важно проверить: Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится: Бельгия
Следующий шаг: Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →

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Официальное название

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.

Обзор

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.

Подробное описание

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.

Вмешательства

Другое Retrospective Group
For the retrospective group of 20 patients.
Другое Prospective Group
Follow-up of the patients for the prospective group

Первичные конечные точки

ROC-AUC [Срок оценки: 2027]

Вторичные конечные точки (1)

F1-score [Срок оценки: When the data will be avalaible, at the end of 2027]

Критерии участия

Критерии включения

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