[HTML][HTML] Disease-course adapting machine learning prognostication models in elderly patients critically ill with COVID-19: multicenter cohort study with external …
C Jung, B Mamandipoor, J Fjølner… - JMIR medical …, 2022 - medinform.jmir.org
Background The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care
systems globally. The disease disproportionately affects the elderly population, both in terms …
systems globally. The disease disproportionately affects the elderly population, both in terms …
[HTML][HTML] Predictive models of mortality for hospitalized patients with COVID-19: retrospective cohort study
Background: The novel coronavirus SARS-CoV-2 and its associated disease, COVID-19,
have caused worldwide disruption, leading countries to take drastic measures to address …
have caused worldwide disruption, leading countries to take drastic measures to address …
COVID-19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African …
Background COVID-19 remains a complex disease in terms of its trajectory and the diversity
of outcomes rendering disease management and clinical resource allocation challenging …
of outcomes rendering disease management and clinical resource allocation challenging …
[HTML][HTML] Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study
Background The COVID-19 pandemic continues to overwhelm intensive care units (ICUs)
worldwide, and improved prediction of mortality among COVID-19 patients could assist …
worldwide, and improved prediction of mortality among COVID-19 patients could assist …
Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19
Objectives To develop and validate a model for prediction of near-term in-hospital mortality
among patients with COVID-19 by application of a machine learning (ML) algorithm on time …
among patients with COVID-19 by application of a machine learning (ML) algorithm on time …
Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19
As predicting the trajectory of COVID-19 is challenging, machine learning models could
assist physicians in identifying high-risk individuals. This study compares the performance of …
assist physicians in identifying high-risk individuals. This study compares the performance of …
Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19
S Garcia-Gutiérrez, C Esteban-Aizpiri, I Lafuente… - Scientific reports, 2022 - nature.com
Despite the publication of great number of tools to aid decisions in COVID-19 patients, there
is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a …
is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a …
Automated ML Techniques for Predicting COVID-19 Mortality in the ICU
A Sakagianni, C Koufopoulou, D Kalles… - … with Informatics and …, 2023 - ebooks.iospress.nl
The COVID-19 infection is still a serious threat to public health and healthcare systems.
Numerous practical machine learning applications have been investigated in this context to …
Numerous practical machine learning applications have been investigated in this context to …
Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods
Objectives The coronavirus disease 2019 (COVID-19) is a resource-intensive global
pandemic. It is important for healthcare systems to identify high-risk COVID-19-positive …
pandemic. It is important for healthcare systems to identify high-risk COVID-19-positive …
Limited applicability of a COVID-19 specific mortality prediction rule to the intensive care setting
C Dupuis, E De Montmollin, M Neuville… - Nature Machine …, 2021 - nature.com
The least bad results were obtained at day 28, with a precision of 37%(positive predictive
value) and an accuracy of 43%, but a recall of 93%(negative predictive value). This decision …
value) and an accuracy of 43%, but a recall of 93%(negative predictive value). This decision …
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