Article

Decision trees for COVID-19 prognosis learned from patient data: Desaturating the ER with Artificial Intelligence

2022-05-10

Abstract excerpt

<h4>Objectives</h4> To present a model that enhances the accuracy of clinicians when presented with a possibly critical Covid-19 patient. <h4>Methods</h4> A retrospective study was performed with information of 5,745 SARS-CoV2 infected patients admitted to the Emergency room of 4 public Hospitals in Madrid belonging to Quirón Salud Health Group (QS) from March 2020 to February 2021. Demographics, clinical variable...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 68fef20f-24ef-5e7c-98bd-e7924a86a81e
DOI: 10.1101/2022.05.09.22274832

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Decision trees for COVID-19 prognosis learned from patient data: Desaturating the ER with Artificial IntelligenceDOI 10.1101/2022.05.09.22274832Read

Using Automated-Machine Learning to Predict COVID-19 Patient Survival: Identify Influential BiomarkersSemantic proximity Read

Multicenter analysis of COVID-19 hospitalizations and stacking machine learning algorithms for prediction of high-risk patientsSemantic proximity Read

Artificial intelligence driven assessment of routinely collected healthcare data is an effective screening test for COVID-19 in patients presenting to hospitalSemantic proximity Read

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, BrazilSemantic proximity Read

Predicting critical illness on initial diagnosis of COVID-19 based on easily-obtained clinical variables: Development and validation of the PRIORITY modelSemantic proximity Read

Development of an Accurate AI/ML-Driven COVID-19 Patient Stratification Model Predicting Disease Outcomes from Disproportionate and Scarce Clinical DatasetsSemantic proximity Read

Comparing Machine Learning Algorithms for Predicting ICU Admission and Mortality in COVID-19Semantic proximity Read

COVID-19 Mortality Risk Prediction using Clinical and Laboratory Examination: Machine Learning Approach for ImplementationSemantic proximity Read

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergencySemantic proximity Read

Machine Learning-Based COVID-19 Patients Triage Algorithm Using Patient-Generated Health Data From Nationwide Multicenter DatabaseSemantic proximity Read

Risk factors for mortality among hospitalized patients with COVID-19Semantic proximity Read

Development of a Real-Time Risk Model (RTRM) for Predicting In-Hospital COVID-19 MortalitySemantic proximity Read