Summary of Machine Learning and Constraint Programming For Efficient Healthcare Scheduling, by Aymen Ben Said and Malek Mouhoub
Machine Learning and Constraint Programming for Efficient Healthcare Scheduling
by Aymen Ben Said, Malek Mouhoub
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the Nurse Scheduling Problem (NSP), which involves assigning nurses to daily shifts while satisfying workload constraints and optimizing hospital costs and nurse preferences. The authors propose two approaches to solve the NSP: implicit and explicit. The implicit approach relies on machine learning methods using historical data to learn patterns that satisfy the constraints and objectives, quantified by the Frobenius Norm. To compensate for uncertainty in this approach, the authors develop an explicit approach using Constraint Satisfaction Problem (CSP) framework, Stochastic Local Search methods, and a new Branch and Bound algorithm with constraint propagation techniques and variables/values ordering heuristics. Additionally, they propose a data-driven approach to passively learn the NSP as a constraint network, which can be solved using their proposed methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about solving a problem called Nurse Scheduling Problem. It’s like scheduling shifts for nurses in hospitals. The goal is to make sure that nurses have the right workload and are happy with their schedules while also keeping costs low. The authors come up with two ways to solve this problem: one uses machine learning to learn patterns from past data, and another uses a more traditional approach called Constraint Satisfaction Problem. They also show how to use these methods to learn and improve over time. |
Keywords
» Artificial intelligence » Machine learning
