Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Objective To review and appraise the validity and usefulness of published and preprint
reports of prediction models for prognosis of patients with covid-19, and for detecting people …
reports of prediction models for prognosis of patients with covid-19, and for detecting people …
The association of smoking status with SARS‐CoV‐2 infection, hospitalization and mortality from COVID‐19: a living rapid evidence review with Bayesian meta …
Aims To estimate the association of smoking status with rates of (i) infection,(ii)
hospitalization,(iii) disease severity and (iv) mortality from SARS‐CoV‐2/COVID‐19 disease …
hospitalization,(iii) disease severity and (iv) mortality from SARS‐CoV‐2/COVID‐19 disease …
Comparing machine learning algorithms for predicting COVID-19 mortality
K Moulaei, M Shanbehzadeh… - BMC medical informatics …, 2022 - Springer
Background The coronavirus disease (COVID-19) hospitalized patients are always at risk of
death. Machine learning (ML) algorithms can be used as a potential solution for predicting …
death. Machine learning (ML) algorithms can be used as a potential solution for predicting …
[HTML][HTML] Development of a prognostic model for mortality in COVID-19 infection using machine learning
AL Booth, E Abels, P McCaffrey - Modern Pathology, 2021 - Elsevier
Abstract Coronavirus disease 2019 (COVID-19) is a novel disease resulting from infection
with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has quickly …
with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has quickly …
Sex-specific evaluation and redevelopment of the GRACE score in non-ST-segment elevation acute coronary syndromes in populations from the UK and Switzerland …
Summary Background The Global Registry of Acute Coronary Events (GRACE) 2.0 score
was developed and validated in predominantly male patient populations. We aimed to …
was developed and validated in predominantly male patient populations. We aimed to …
Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying
Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-
Cov2 virus has become the greatest health and controversial issue for worldwide nations. It …
Cov2 virus has become the greatest health and controversial issue for worldwide nations. It …
[HTML][HTML] Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation
Background COVID-19 has infected millions of people worldwide and is responsible for
several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful …
several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful …
Challenges and prospects of visual contactless physiological monitoring in clinical study
The monitoring of physiological parameters is a crucial topic in promoting human health and
an indispensable approach for assessing physiological status and diagnosing diseases …
an indispensable approach for assessing physiological status and diagnosing diseases …
Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases
A Syrowatka, M Kuznetsova, A Alsubai… - NPJ digital …, 2021 - nature.com
Artificial intelligence (AI) represents a valuable tool that could be widely used to inform
clinical and public health decision-making to effectively manage the impacts of a pandemic …
clinical and public health decision-making to effectively manage the impacts of a pandemic …
Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using …
Background Predicting outcomes of patients with COVID-19 at an early stage is crucial for
optimised clinical care and resource management, especially during a pandemic. Although …
optimised clinical care and resource management, especially during a pandemic. Although …