[Development of an IBD patient population dashboard to monitor and improve care through machine learning]

Objective

Development of a ML model to predict disease progression and treatment outcome for patients with Crohn’s disease or ulcerative colitis. The goal is to improve care in combination with a monitoring tool in the form of a patient population dashboard.

Methodology

Extracting relevant information from the UR-Care database and from the Electronic Health Record (EHR) system of AZ Delta to visualize patient and patient population characteristics in a dashboard. The project also explores the potential of combining the dashboard with a clinical decision support system by training explainable ML models to predict disease outcomes.

Impact and future directions

Analysis of the ML models in combination with the dashboard might support physicians in finding the best suited treatment options for specific patients.

General info and contact

Keywords (#): IBD, Machine learning, Patient population dashboard

RADar project research lead: Ir. Hanne Vanluchene

RADar project researchers: Dr. Depestele Siel, M.Sc. Werbrouck Joica

Principal investigator: MD Filip Baert, PhD

Timeline: 2024-2025

Status: Research ongoing

Funding: Takeda Belgium NV

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