Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters.
COVID-19
Cox regression
Kaplan–Meier
hematochemical parameters
machine learning
prognostic markers
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
20 Dec 2021
20 Dec 2021
Historique:
received:
22
11
2021
revised:
15
12
2021
accepted:
17
12
2021
entrez:
28
12
2021
pubmed:
29
12
2021
medline:
30
12
2021
Statut:
epublish
Résumé
The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan-Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was
Identifiants
pubmed: 34960595
pii: s21248503
doi: 10.3390/s21248503
pmc: PMC8705488
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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