125. An Online Algorithm for Integrated Scheduling of Pre-Treatment and Treatment Appointments in Radiotherapy using Deep Reinforcement Learning
Invited abstract in session ME-2: Cancer and personalised care, stream Sessions.
Monday, 15:30-17:00Room: NTNU, Realfagbygget R8
Authors (first author is the speaker)
| 1. | Maxim Frankish
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| Business Informatics and Operations Management, Ghent University | |
| 2. | Broos Maenhout
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| Business Informatics and Operations Management, Ghent University |
Abstract
Scheduling patients for radiotherapy within the oncology department is vital for ensuring timely and effective treatment. The patient’s therapy consists of a sequence of pre-treatment consultations, i.e. preliminary investigations to define the required treatment, followed by the radiotherapy treatment sessions on linear accelerators. Timely scheduling of pre-treatment consultations is crucial to prevent unnecessary delays in radiotherapy treatment, thereby impacting patient health. Therefore, we suggest integrating both scheduling problems. We employ a Reinforcement Learning-based scheduling technique to solve the multi-appointment scheduling problem in an online fashion. We design a reward function comprising three components to guide the agent towards learning an effective scheduling policy involving multiple appointments. Additionally, we compare our proposed model with benchmark online heuristics inspired by the ASAP procedure. We further provide insight into the reward function design, which steers the agent to learn a feasible policy that leads to high-quality solutions. Experiments show that our algorithm outperforms the benchmark heuristics.
Keywords
- (Multi) appointment scheduling
- Artificial Intelligence
Status: accepted
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