Application of Machine Learning in Radiation Oncology Scheduling

The Ophthalmology department of CHUM receives almost 400 patients per day, each of which needs to go through different tests and consultations. Scheduling those appointments is a complicated task as it involves multiple shared resources, precedent constraints between tests, and uncertainty in tests’ durations, cancellations, emergency patients, etc. The current manual scheduling at the department results in high fluctuations in workload between days, inefficient use of resources, and long waiting time for patients.
In this project, we aim to propose an optimization model to schedule patients’ appointments. The problem is modelled as a multi-appointment, multi-resources patient scheduling problem. The second phase of the project aims to utilise machine learning tools to measure stochastic factors of the problem and to integrate that information into a decision-making framework to deal with uncertainty in scheduling.

Faculty Supervisor:

Antoine Legrain

Student:

Partner:

Centre de recherche du CHUM

Discipline:

Mathematics

Sector:

Health and Related Sciences & Technology

University:

Polytechnique Montréal

Program: