CPAS uses machine learning to plan hospital resources for COVID-19.
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Study predicts future hospitalizations to manage COVID-19 patient surge.
Bayesian model forecasts hospital resource use during pandemic.
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…
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care. We studied this problem using eight years of Electronic Health Records (EHR) data from Stanford Hospital. We fit models to predict 24 hour discharge across the entire inpatient population. The best …
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
This work proposes a student-teacher network for predicting hospital admission locations.
A new model improves homogeneity in burn patient reimbursement.
State-augmented algorithm optimizes wireless network resource management.
Deep learning enhances water resources management through data analysis.
New model predicts ICU patients' stay duration efficiently.
Study optimizes natural resource harvesting under model uncertainty using risk measures.
This paper optimizes revenue and resource balance in network revenue management.
Study uses data to analyze COPD patients' impact on hospital systems.
Optimizes wireless network resource management with state-augmented policies.
Study proposes managing COVID-19 without economy shutdowns.
Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.
HL algorithms improve resource allocation in cloud environments.
Accurately predicting patients' risk of 30-day hospital readmission would enable hospitals to efficiently allocate resource-intensive interventions. We develop a new method, Categorical Co-Frequency Analysis (CoFA), for clustering diagnosis codes from the International Classification of Diseases (ICD) according to the …
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…
New model predicts ICU patient stays more accurately.
Reducing the incidence of surgical site infections (SSIs) is one of the objectives of the French nosocomial infection control program. Manual monitoring of SSIs is carried out each year by the hospital hygiene team and surgeons at the University Hospital of Bordeaux. Our goal was to develop an automatic detection algor…
Paper tackles pandemic resource allocation challenges.
Graph neural networks optimize radio resource management policies for wireless networks.
The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and longstanding problem in hospital management. Majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients througho…
Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine…
Tree-based model averaging improves CATE estimation from diverse sites.
Enhances multi-project scheduling with multiple priority rules.
In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resourc…
Research develops a water quality prediction model using LSTM.
Meta-DRL improves resource allocation in O-RAN networks.
This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…
EERO optimizes resource usage for efficient classification.
The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository. This process necessitat…
Unified ML approach predicts ED attendances with high accuracy.
ATA optimizes task allocation in distributed machine learning.
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…
Efficient human resource management needs accurate assessment and representation of available competences as well as effective mapping of required competences for specific jobs and positions. In this regard, appropriate definition and identification of competence gaps express differences between acquired and required c…
A deep RL framework optimizes resource allocation in wireless networks.
Optimizes data power control in cell-free networks for better spectral efficiency.
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…
Transportation systems can be conceptualized as an instrument of spreading people and resources over the territory, playing an important role in developing sustainable cities. The current rationale of transport provision is based on population demand, disregarding land use and socioeconomic information. To meet the cha…
Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admit ward-bed to receive a patient is delayed until an admission decision is made by a doctor. To reduce the overcrowding and waiting time of E…
Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resources in order to maximize either the average or the worst-case performance. However, joint consideration of both metrics is often neglected as …
The study uses stock market indicators to forecast COVID-19 cases.
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, -Carving Decision Chain (ACDC), sequentially carves o…
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints. Due to the large-scale and dynamic nature of resource coordination in hospitals and factories, human domain experts manually plan a…
Bayesian models forecast COVID-19 hospitalizations at single sites.