Related papers: Simulation of Patient Flow in Multiple Healthcare …
Cardiac amyloidosis, a rare and highly morbid condition, presents significant challenges for detection through echocardiography. Recently, there has been a surge in proposing machine-learning algorithms to identify cardiac amyloidosis, with…
Mental healthcare is one of the prominent parts of the healthcare industry with alarming concerns related to patients depression, stress leading to self-harm and threat to fellow patients and medical staff. To provide a therapeutic…
Sepsis-induced acute respiratory failure (ARF) is a serious complication with a poor prognosis. This paper presents a deep representation learningbased phenotyping method to identify distinct groups of clinical trajectories of septic…
The simulation of the dynamical behavior of pedestrians and crowds in spatial structures is a consolidated research and application context that still presents challenges for researchers in different fields and disciplines. Despite…
Computer-based scientific experiments are becoming increasingly data-intensive, necessitating the use of High-Performance Computing (HPC) clusters to handle large scientific workflows. These workflows result in complex data and control…
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor…
The last few decades, crowd simulation for crisis management is highlighted as an important topic of interest for many scientific fields. As the continues evolution of computational resources increases, along with the capabilities of…
Responding rapidly to a patient who is demonstrating signs of imminent clinical deterioration is a basic tenet of patient care. This gave rise to a patient safety intervention philosophy known as a Rapid Response System (RRS), whereby a…
GPUs are widely used to accelerate many important classes of workloads today. However, we observe that several important emerging classes of workloads, including simulation engines for deep reinforcement learning and dynamic neural…
Objective: Machine learning algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few…
Automated material handling systems are used in healthcare facilities to optimize material flow, minimize workforce requirements, reduce the risk of contamination, and reduce injuries. This study proposes a framework that integrates data…
Emergency Departments (EDs) overcrowding is a well recognized worldwide phenomenon. The consequences range from long waiting times for visits and treatment of patients up to life-threatening health conditions. The international community is…
In this work, we investigate how concepts from medical flow visualization can be applied to enhance stroke prevention diagnostics. Our focus lies on carotid stenoses, i.e., local narrowings of the major brain-supplying arteries, which are a…
Patient similarity computation (PSC) is a fundamental problem in healthcare informatics. The aim of the patient similarity computation is to measure the similarity among patients according to their historical clinical records, which helps…
Background: Artificial Intelligence (AI) clinical decision support (CDS) systems have the potential to augment surgical risk assessments, but successful adoption depends on an understanding of end-user needs and current workflows. This…
Purpose: Computational Fluid Dynamics (CFD) simulations are performed to investigate the impact of adding a grid to a two-inlet dry powder inhaler (DPI). The purpose of the paper is to show the importance of the correct choice of closure…
Doctors often make diagonostic decisions based on patient's image scans, such as magnetic resonance imaging (MRI), and patient's electronic health records (EHR) such as age, gender, blood pressure and so on. Despite a lot of automatic…
Acute Coronary Syndrome (ACS) is a life-threatening cardiovascular condition where early and accurate diagnosis is critical for effective treatment and improved patient outcomes. This study explores the use of ECG foundation models,…
The widespread adoption of Electronic Health Record (EHR) systems in healthcare institutes has generated vast amounts of medical data, offering significant opportunities for improving healthcare services through deep learning techniques.…
The automatic discovery of a model to represent the history of encounters of a group of patients with the healthcare system -- the so-called "pathway of patients" -- is a new field of research that supports clinical and organisational…