相关论文: An Artificial Intelligence-Based Framework for Pre…
The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff. Most critical is the efficient allocation of resource-heavy Intensive Care Unit (ICU) beds to 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…
Public charging station occupancy prediction plays key importance in developing a smart charging strategy to reduce electric vehicle (EV) operator and user inconvenience. However, existing studies are mainly based on conventional…
The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff. Most critical is the efficient allocation of resource-heavy Intensive Care Unit (ICU) beds to the…
Myocardial infarction (MI) is a leading cause of death, and its adverse outcomes are urgent to predict. Yet ECG-based prognostic models underperform because deep learning requires large, labelled datasets, which are scarce in medicine.…
Modeling the arrival process to an Emergency Department (ED) is the first step of all studies dealing with the patient flow within the ED. Many of them focus on the increasing phenomenon of ED overcrowding, which is afflicting hospitals all…
The widespread digitization of patient data via electronic health records (EHRs) has created an unprecedented opportunity to use machine learning algorithms to better predict disease risk at the patient level. Although predictive models…
Recent behavioral and electroencephalograph (EEG) studies have defined ways that auditory spatial attention can be allocated over large regions of space. As with most experimental studies, behavior EEG was averaged over 10s of minutes…
Background Predicting mortality and resource utilization from electronic health records (EHRs) is challenging yet crucial for optimizing patient outcomes and managing costs in intensive care unit (ICU). Existing approaches predominantly…
In this paper, we propose an ETA model (Estimated Time of Arrival) that leverages an attention mechanism over historical road speed patterns. As autonomous driving and intelligent transportation systems become increasingly prevalent, the…
The objective of this work is to develop an Electronic Medical Record (EMR) data processing tool that confers clinical context to Machine Learning (ML) algorithms for error handling, bias mitigation and interpretability. We present…
Short-term load forecasting for AI data centers presents new challenges because it is computing-driven, with heterogeneous job arrivals, sizes, and durations exhibiting bursty, non-stationary dynamics. Compared with traditional load types,…
Overcrowding in emergency departments (ED) is a persistent problem exacerbated by population growth, emergence of pandemics, and increased morbidity and mortality rates. Thus, automated approaches for ED layout design have recently emerged…
The electroencephalogram (EEG) is one of the most precious technologies to understand the happenings inside our brain and further understand our body's happenings. Automatic prediction of oncoming seizures using the EEG signals helps the…
Deep learning (DL) 3D dose prediction has recently gained a lot of attention. However, the variability of plan quality in the training dataset, generated manually by planners with wide range of expertise, can dramatically effect the quality…
This work develops a methodology for studying the effect of an offload zone on the ambulance ramping problem using a multi-server, multi-class non-preemptive priority queueing model that can be treated analytically. A prototype model for…
Background/Objectives: Efficient task allocation in hospital emergency departments (EDs) is critical for operational efficiency and patient care quality, yet the complexity of staff coordination poses significant challenges. This study…
Clinician scheduling remains a persistent challenge due to limited clinical resources and fluctuating demands. This complexity is especially acute in large academic anesthesiology departments as physicians balance responsibilities across…
Continuous edge inference necessitates not merely low per-timeslot latency, but sustained timeliness guarantees in the presence of time-varying channels, fluctuating edge workloads, and coupled bandwidth-computing resource constraints.…
This study investigates the use of a large language model system to improve efficiency and quality in emergency department (ED) discharge letter writing. Time constraints and infrastructural deficits make compliance with current discharge…