相关论文: Multi-task Prediction of Patient Workload

In the era of "information overload", effective information provision is essential for enabling rapid response and critical decision making. In making sense of diverse information sources, data dashboards have become an indispensable tool,…

Crowdsourcing has emerged as an effective means for performing a number of machine learning tasks such as annotation and labelling of images and other data sets. In most early settings of crowdsourcing, the task involved classification,…

Risk prediction is central to both clinical medicine and public health. While many machine learning models have been developed to predict mortality, they are rarely applied in the clinical literature, where classification tasks typically…

In a warehouse environment, tasks appear dynamically. Consequently, a task management system that matches them with the workforce too early (e.g., weeks in advance) is necessarily sub-optimal. Also, the rapidly increasing size of the action…

Increasing the efficiency and effectiveness of the healthcare system is a challenge faced worldwide. Many outpatient clinics have implemented two-stage service systems, with both a physician and physician assistant, to enhance capacity and…

We address the problem of predicting whether sufficient memory and CPU resources have been requested for jobs at submission time. For this purpose, we examine the task of training a supervised machine learning system to predict the outcome…

We study the classic fully dynamic load balancing problem on unrelated machines where jobs arrive and depart over time and the goal is minimizing the maximum load, or more generally the l_p-norm of the load vector. Previous work either…

Worldwide, several cases go undiagnosed due to poor healthcare support in remote areas. In this context, a centralized system is needed for effective monitoring and analysis of the medical records. A web-based patient diagnostic system is a…

Recent advances in the interconnectedness and digitization of industrial machines, known as Industry 4.0, pave the way for new analytical techniques. Indeed, the availability and the richness of production-related data enables new…

Artificial intelligence (AI) systems increasingly achieve expert-level predictive accuracy in healthcare, yet improvements in model performance often fail to produce corresponding gains in patient outcomes. We term this disconnect the…

Hospitals, due to their complexity and unique requirements, play a pivotal role in global energy consumption patterns. This study conducted a comprehensive literature review, utilizing the PRISMA framework, of articles that employed machine…

Nursing homes (NHs) are critical healthcare infrastructures for caring frail older adults with 24/7 formal care and personal assistance. Adequate NH resource planning is of great importance to ensure desired quality of care and resident…

Machine learning shows great potential in virtual screening for drug discovery. Current efforts on accelerating docking-based virtual screening do not consider using existing data of other previously developed targets. To make use of the…

Research is a tertiary priority in the EHR, where the priorities are patient care and billing. Because of this, the data is not standardized or formatted in a manner easily adapted to machine learning approaches. Data may be missing for a…

Cloud providers introduce features (e.g., Spot VMs, Harvest VMs, and Burstable VMs) and optimizations (e.g., oversubscription, auto-scaling, power harvesting, and overclocking) to improve efficiency and reliability. To effectively utilize…

Accurate prediction of application performance is critical for enabling effective scheduling and resource management in resource-constrained dynamic edge environments. However, achieving predictable performance in such environments remains…

We propose to meta-learn an a self-supervised patient trajectory forecast learning rule by meta-training on a meta-objective that directly optimizes the utility of the patient representation over the subsequent clinical outcome prediction.…

Efficient virtual machine load balancing scheduling is crucial in cloud computing to optimize resource utilization and system performance. To address this issue, several load balancing scheduling algorithms have been proposed, including…

A parallel computer system is a collection of processing elements that communicate and cooperate to solve large computational problems efficiently. To achieve this, at first the large computational problem is partitioned into several tasks…

Overcrowding in emergency departments (ED) remains a persistent operational challenge worldwide, causing delays in care delivery and downstream congestion. ED boarding time, defined as the duration admitted patients remain in the ED while…