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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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265177102 · Jun 202019922001200920172026
48 results for Hospital Resource Management

Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on…

2019-09-12abs ↗pdf ↗

Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.

problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.

This work proposes a student-teacher network for predicting hospital admission locations.

problem Accurate prediction of hospital admission locations to optimize resource allocation.
method Reinforcement learning approach where a teacher network selects data batches for a student network.
result The approach outperforms state-of-the-art methods on tabular data and image recognition.

A new model improves homogeneity in burn patient reimbursement.

problem Incomplete homogeneity checks for burn patients using LOS as a proxy.
method Cost-sensitive decision tree model considering patient-level cost and severity of burn.
result Identified groups with increased homogeneity compared to current HRG groups.

New model predicts ICU patients' stay duration efficiently.

problem Efficient ICU bed allocation under resource constraints.
method Temporal Pointwise Convolution (TPC) model combining temporal and pointwise convolutions.
result Significant performance improvements over LSTM and Transformer models.

Study optimizes natural resource harvesting under model uncertainty using risk measures.

problem Optimal harvesting policy selection for natural resources under model uncertainty.
method Investigated using neoclassical growth model dynamics and convex risk measures, specifically Fréchet risk measures.
result Robust harvesting strategies quantifying operational and marginal risk under model uncertainty.

This paper optimizes revenue and resource balance in network revenue management.

problem Maximizing revenue while ensuring fair resource consumption across different suppliers.
method Introduces a regularized revenue objective and a primal-dual UCB algorithm for continuous prices and balancing.
result Achieves a worst-case regret of O~(N5/2T)\widetilde O(N^{5/2}\sqrt{T}) for revenue maximization and balancing.

Study uses data to analyze COPD patients' impact on hospital systems.

problem Understanding and quantifying resource requirements for COPD patients.
method Combines segmentation, queuing theory, and data recovery techniques.
result Finding useful operational results from incomplete administrative data.

Optimizes wireless network resource management with state-augmented policies.

problem Optimizing network-wide utility with user performance constraints.
method State-augmented parameterization of RRM policy, using dual variables.
result Superior trade-off between mean, minimum, and 5th percentile rates.

Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.

problem Source heterogeneity makes it hard to use multiple related auxiliary sources effectively.
method Trans-GLMC constructs clusters of sources, then combines global fusion, within-cluster refinement, and target debiasing.
result Improves facility-specific prediction and identifies interpretable communities of hospitals with mutual transferability.

HL algorithms improve resource allocation in cloud environments.

problem Sequential decision-making under uncertainty with exogenous variables.
method HL algorithms leverage exogenous variable samples to infer counterfactual consequences.
result HL algorithms outperform classic methods and reinforcement learning in resource allocation.

As most natural resources, fisheries are affected by random disturbances. The evolution of such resources may be modelled by a succession of deterministic process and random perturbations on biomass and/or growth rate at random times. We analyze the impact of the characteristics of the perturbations on the management o…

2019-09-04abs ↗pdf ↗

New model predicts ICU patient stays more accurately.

problem Efficient ICU bed allocation under resource constraints.
method Temporal Pointwise Convolutional Networks (TPC) combining temporal and pointwise convolutions.
result Significant performance improvements over LSTM and Transformer models.

Graph neural networks optimize radio resource management policies for wireless networks.

problem Optimizing user selection and power control in wireless networks with fairness constraints.
method Formulated as a Lagrangian dual problem, RRM policies are parameterized by a GNN architecture trained on channel conditions.
result The method achieves superior tradeoff between average and 5th percentile rates, demonstrating fairness.

Tree-based model averaging improves CATE estimation from diverse sites.

problem Limited sample size and privacy concerns prevent accurate personalized treatment effect estimation.
method Tree-based model averaging approach to estimate CATEs from multiple heterogeneous sites.
result Improved accuracy in estimating conditional average treatment effects (CATEs) across sites.

Enhances multi-project scheduling with multiple priority rules.

problem Resource allocation in multi-project scheduling with limited time and resources.
method Simulation-based approach using composite priority rules.
result Increased probability of finding schedules with shortest duration.

EERO optimizes resource usage for efficient classification.

problem Managing computational resources in complex machine learning models.
method EERO uses multiple classifiers with a reject option to adaptively shorten processing paths.
result EERO effectively manages budget allocation and enhances accuracy in complex scenarios.

Unified ML approach predicts ED attendances with high accuracy.

problem Managing hospital demand at emergency departments efficiently.
method Ensemble of time series and machine learning approaches with hyperparameter tuning.
result Predictions with mean absolute error of +/- 14 and +/- 10 patients, MAE of 6.8% and 8.6%.

ATA optimizes task allocation in distributed machine learning.

problem Greedy task allocation leads to inefficiencies in distributed machine learning.
method Adaptive Task Allocation (ATA) adapts to unknown computation time distributions.
result ATA identifies optimal task allocation without prior knowledge of computation times.

Floating Content (FC) is a communication paradigm for the local dissemination of contextualized information through D2D connectivity, in a way which minimizes the use of resources while achieving some specified performance target. Existing approaches to FC dimensioning are based on unrealistic system assumptions that m…

2018-10-24abs ↗pdf ↗

A deep RL framework optimizes resource allocation in wireless networks.

problem Optimizing resource allocation and interference in wireless networks.
method Multi-agent deep reinforcement learning for distributed decision-making.
result Our approach outperforms decentralized and centralized baselines in terms of user rates.

Optimizes data power control in cell-free networks for better spectral efficiency.

problem Maximizing overall spectral efficiency in cell-free networks with multi-objective optimisation.
method Applied scalable multi-objective Bayesian optimisation to solve convergence-time limitations.
result Improved radio resource management in cell-free networks.

In many healthcare settings, intuitive decision rules for risk stratification can help effective hospital resource allocation. This paper introduces a novel variant of decision tree algorithms that produces a chain of decisions, not a general tree. Our algorithm, αα-Carving Decision Chain (ACDC), sequentially carves o…

2016-06-16abs ↗pdf ↗

Bayesian models forecast COVID-19 hospitalizations at single sites.

problem Forecasting daily COVID-19 hospitalizations at a single hospital.
method Hierarchical Bayesian models with generalized Poisson likelihood and autoregressive/Gaussian process latent processes.
result Demonstrated superior performance compared to baselines in public datasets.