Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review
Aim To perform a systematic review on the use of Artificial Intelligence (AI) techniques for
predicting COVID-19 hospitalization and mortality using primary and secondary data …
predicting COVID-19 hospitalization and mortality using primary and secondary data …
A new approach to digital health? Virtual COVID-19 care: A scoping review
L Chung-Lee, C Catallo - Digital Health, 2023 - journals.sagepub.com
Aims The use of virtual care enabled by digital technologies has increased, prompted by
public health restrictions in response to COVID-19. Non-hospitalized persons in the acute …
public health restrictions in response to COVID-19. Non-hospitalized persons in the acute …
Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways
The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a
triage of patients has been discussed controversially primarily through an ethical …
triage of patients has been discussed controversially primarily through an ethical …
[HTML][HTML] Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data
TW Campbell, MP Wilson, H Roder… - International journal of …, 2021 - Elsevier
Rationale: Prognostic tools for aiding in the treatment of hospitalized COVID-19 patients
could help improve outcome by identifying patients at higher or lower risk of severe disease …
could help improve outcome by identifying patients at higher or lower risk of severe disease …
Specific risk factors for fatal outcome in critically ill COVID-19 patients: results from a European multicenter study
D Meintrup, S Borgmann, K Seidl, M Stecher… - Journal of Clinical …, 2021 - mdpi.com
(1) Background: The aim of our study was to identify specific risk factors for fatal outcome in
critically ill COVID-19 patients.(2) Methods: Our data set consisted of 840 patients enclosed …
critically ill COVID-19 patients.(2) Methods: Our data set consisted of 840 patients enclosed …
Development of machine-learning model to predict COVID-19 mortality: application of ensemble model and regarding feature impacts
SM Baik, M Lee, KS Hong, DJ Park - Diagnostics, 2022 - mdpi.com
This study was designed to develop machine-learning models to predict COVID-19 mortality
and identify its key features based on clinical characteristics and laboratory tests. For this …
and identify its key features based on clinical characteristics and laboratory tests. For this …
[HTML][HTML] Development and validation of a machine learning COVID-19 veteran (COVet) deterioration risk score
S Govindan, A Spicer, M Bearce… - Critical Care …, 2024 - journals.lww.com
RESULTS: A total of 96,908 admissions occurred during the study period, of which 59,897
were in the Veteran sample and 37,011 were in the non-Veteran sample. During external …
were in the Veteran sample and 37,011 were in the non-Veteran sample. During external …
Early prediction model for critical illness of hospitalized COVID-19 patients based on machine learning techniques
Motivation Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical
illness suddenly is a matter of great concern. Early identification and effective triaging of …
illness suddenly is a matter of great concern. Early identification and effective triaging of …
Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19
Y Kogan, A Robinson, E Itelman, Y Bar-Nur… - Scientific Reports, 2022 - nature.com
Our study was aimed at developing and validating a new approach, embodied in a machine
learning-based model, for sequentially monitoring hospitalized COVID-19 patients and …
learning-based model, for sequentially monitoring hospitalized COVID-19 patients and …
[HTML][HTML] Early Warning Systems for Acute Respiratory Infections: Scoping Review of Global Evidence
A Patel, K Maruthananth, N Matharu… - JMIR Public Health …, 2024 - publichealth.jmir.org
Background Early warning systems (EWSs) are tools that integrate clinical observations to
identify patterns indicating increased risks of clinical deterioration, thus facilitating timely and …
identify patterns indicating increased risks of clinical deterioration, thus facilitating timely and …