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arXiv Machine Learning By Bozhi Sun, Seda Tierney, Jeffrey A. Feinstein, Frederick Damen, Alison L. Marsden, Daniele E. Schiavazzi

Rule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study

Read the original on arXiv Machine Learning →

The study evaluates scheduling strategies for echocardiographic exams in a pediatric echo lab, comparing on-the-fly allocation with reservation-based approaches using real operational data from Stanford’s Lucile Packard Children’s Hospital. On-the-fly allocation consistently reduces patient waiting times across all tested reservation configurations. A reinforcement‑learning model (Dueling Double DQN) was trained to approximate an optimal on-the-fly policy but did not outperform the best rule‑based on-the-fly strategy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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