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.
arXiv:2607. 15432v1 Announce Type: cross Abstract: Emergency department (ED) boarding occurs when admitted patients remain in the ED while awaiting inpatient beds.
arXiv:2606. 30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.
The study evaluates whether machine learning-guided proactive activation of Full Capacity Protocols (FCPs) can better control emergency department (ED) overcrowding compared to conventional reactive activation. Using a validated machine learning and discrete-event simulation framework, the authors predicted hourly waiting-room crowding six hours ahead and triggered proactive FCPs. Across a 365‑day simulation with 10 replications, proactive deployment significantly reduced the percentage of time above the crowding threshold and average protocol duration, lowered crowding variability and extreme congestion events, and produced smoother ED operations, though improvements in length of stay and wait time were not statistically significant.
arXiv:2608. 04669v1 Announce Type: new Abstract: Many social services assign scarce resources, such as housing assistance or hospital interventions, to people who arrive one at a time: each arrival must receive a decision immediately, and the long-run usage of every resource must stay within its capacity.
The study evaluates six deep reinforcement learning (DRL) approaches for high‑performance computing (HPC) job scheduling, comparing Proximal Policy Optimization (PPO), Advantage Actor‑Critic (A2C), and Deep Q‑Network (DQN) with and without action masking. Across two real Slurm workload traces—one homogeneous and one heterogeneous—masked DRL treatments complete evaluation, while unmasked ones are truncated. On the heterogeneous trace, MaskablePPO outperforms baseline heuristics in all three metrics, achieving waiting times within 10% of the best heuristics and a 22% lower maximum bounded slowdown than Last‑Come, First‑Served (LCFS).
arXiv:2608.28599v1 Announce Type: new Abstract: Clinical diagnosis is a step-by-step, cost-aware process: a physician orders examinations one at a time, observes the results, and updates the diagnosi...
The paper introduces a Physics‑Informed Multi‑Agent Coordination framework that embeds calibrated BCMP queueing topologies into a decentralized multi‑agent reinforcement learning system for hospital patient flow. It formulates the problem as a Decentralized Partially Observable Markov Decision Process with coupled resource constraints, enabling departmental agents to negotiate patient routing and service scaling while exchanging localized action fingerprints to handle non‑stationarity. Empirical tests on MIMIC‑IV data show the approach reduces cumulative system delay compared to static Markovian models, heuristic dispatching, and independent multi‑agent baselines, all while preserving clinical safety constraints.
Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic. We investigate whether reinforcement learning (RL) can learn adaptive batching and routing policies that outperform these heuristics, training REINFORCE and PPO agents on a discrete-event simulator validated against queuing theory and production traces (Azure Functions, BurstGPT).
arXiv:2607. 05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic.
arXiv:2610.09519v1 Announce Type: new Abstract: Production machine-learning models are derived artifacts of time-bounded training snapshots: a deployed model is a materialized view over a training cu...
arXiv:2608. 07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.
arXiv:2505. 23131v2 Announce Type: replace Abstract: We study the problem of assigning operations in a dataflow graph to devices to minimize execution time in a work-conserving system, with emphasis on complex machine learning workloads.