相关论文: Predicting special care during the COVID-19 pandem…

Introduction: For COVID-19 patients accurate prediction of disease severity and mortality risk would greatly improve care delivery and resource allocation. There are many patient-related factors, such as pre-existing comorbidities that…

AI and Machine Learning can offer powerful tools to help in the fight against Covid-19. In this paper we present a study and a concrete tool based on machine learning to predict the prognosis of hospitalised patients with Covid-19. In…

Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to inadequate staffing levels. These delays have been linked with adverse clinical outcomes.…

At the end of 2019, the latest novel coronavirus Sars-CoV-2 emerged as a significant acute respiratory disease that has become a global pandemic. Countries like Brazil have had difficulty in dealing with the virus due to the high…

Purpose: This paper proposes a methodology and a computational tool to study the COVID-19 pandemic throughout the world and to perform a trend analysis to assess its local dynamics. Methods: Mathematical functions are employed to describe…

COVID-19 has challenged health systems to learn how to learn. This paper describes the context, methods and challenges for learning to improve COVID-19 care at one academic health center. Challenges to learning include: (1) choosing a right…

Existing prognostic tools mainly focus on predicting the risk of mortality among patients with coronavirus disease 2019. However, clinical evidence suggests that COVID-19 can result in non-mortal complications that affect patient prognosis.…

The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 10th, 2020, more than 7.1 million people were infected, and more than 400 thousand have died worldwide. In the…

Purpose: Artificial intelligence (AI) techniques have been extensively utilized for diagnosing and prognosis of several diseases in recent years. This study identifies, appraises and synthesizes published studies on the use of AI for the…

The COVID-19 pandemic has impacted lives and economies across the globe, leading to many deaths. While vaccination is an important intervention, its roll-out is slow and unequal across the globe. Therefore, extensive testing still remains…

Objective. The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related…

Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never…

In this paper, we propose a machine learning technics and SIR models (deterministic and stochastic cases) with numerical approximations to predict the number of cases infected with the COVID-19, for both in few days and the following three…

The COVID-19 pandemic has magnified an already existing trend of people looking for healthcare solutions online. One class of solutions are symptom checkers, which have become very popular in the context of COVID-19. Traditional symptom…

The global pandemic caused by COVID-19 affects our lives in all aspects. As of September 11, more than 28 million people have tested positive for COVID-19 infection, and more than 911,000 people have lost their lives in this virus battle.…

We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University. Our main objective is to provide predictions of the number of infected…

Numerous COVID-19 clinical decision support systems have been developed. However many of these systems do not have the merit for validity due to methodological shortcomings including algorithmic bias. Methods Logistic regression models were…

The COVID-19 pandemic has had far-reaching consequences, highlighting the urgency for explanatory and predictive tools to track infection rates and burden of care over time and space. However, the scarcity and inhomogeneity of data is a…

We consider the problem of forecasting the daily number of hospitalized COVID-19 patients at a single hospital site, in order to help administrators with logistics and planning. We develop several candidate hierarchical Bayesian models…

When we face patients arriving to a hospital suffering from the effects of some illness, one of the main problems we can encounter is evaluating whether or not said patients are going to require intensive care in the near future. This…