Predicting clinical trial success and protocol feasibility with machine learning - ON-1120

Genre de projet: Recherche
Discipline(s) souhaitée(s): Médecine, Sciences de la vie, Informatique, Sciences mathématiques
Entreprise: Banting AI
Durée du projet: Flexible
Date souhaitée de début: Dès que possible
Langue exigée: Flexible
Emplacement(s): ON, Canada
Nombre de postes: 1
Niveau de scolarité désiré: MaîtriseDoctoratRecherche postdoctorale
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: Yes

Au sujet de l’entreprise: 

At Banting AI, we’re dedicated to making clinical trials faster, simpler, and more accessible. We use advanced artificial intelligence to streamline clinical research, helping sponsors and sites accelerate timelines, reduce costs, and deliver innovative treatments to patients more effectively. Founded in Canada, Banting AI is committed to improving healthcare outcomes at home and abroad.

Veuillez décrire le projet.: 

Clinical trial protocols frequently face operational hurdles, underestimated site and patient burdens, and high participant dropout rates, driving increased trial costs and delays. This project will develop and validate machine learning models to proactively evaluate clinical trial feasibility and predict trial success—specifically targeting recruitment targets, participant retention, minimal deviations, and achievement of primary endpoints.

Leveraging retrospective Canadian clinical trial data (historical protocol information, operational outcomes, and performance metrics), this research will explicitly identify critical protocol components predictive of operational challenges, participant burdens, and dropout risks. The goal is not only predictive accuracy but also to directly inform the development of practical, actionable, and improved protocol design methodologies that reduce operational burdens, lower trial-related costs, and enhance overall trial predictability and efficiency.

Expertise ou compétences exigées: 

• Predictive modeling and statistical analysis.
• Practical experience with machine learning frameworks (e.g., TensorFlow, PyTorch).
• Familiarity with clinical trial protocols, operations, and key performance metrics.
• Data management expertise (structured/unstructured clinical datasets).
• Strong collaborative and communication skills.
• Understanding of Canadian healthcare regulations, data governance, and privacy standards (PIPEDA).