In the published article, there was an error in the Abstract section, Methods. This sentence previously stated:
“Methods: The study uses the clinical data of 500 COVID-19 patients from a designated hospital in Guangzhou, China, and selects eight features, including age, sex, dyspnea, comorbidity, complication, lymphocytes (LYM), CRP, and lung injury score, as the most important predictors of COVID-19 severity.”
The corrected sentence appears below:
“Methods: The study uses the clinical data of 500 COVID-19 patients from a designated hospital in Suzhou, China, and selects eight features, including age, sex, dyspnea, comorbidity, complication, lymphocytes (LYM), CRP, and lung injury score, as the most important predictors of COVID-19 severity.”
In the published article, there was also an error in the Discussion section, paragraph 1. This sentence previously stated:
“In this study, we developed an intelligent diagnosis model based on the LASSO method to predict the severity of disease conditions in COVID-19 patients. We collected the clinical data of 500 COVID-19 patients from a designated hospital in Guangzhou, China, and extracted 30 potential features, including demographic, epidemiological, clinical, laboratory, and imaging variables.”
The corrected sentence appears below:
“In this study, we developed an intelligent diagnosis model based on the LASSO method to predict the severity of disease conditions in COVID-19 patients. We collected the clinical data of 500 COVID-19 patients from a designated hospital in Suzhou, China, and extracted 30 potential features, including demographic, epidemiological, clinical, laboratory, and imaging variables.”
A correction has been made to the Discussion section, paragraph 5. This sentence previously stated:
“Our study also has some limitations and directions for future research. First, our data were collected from a single hospital in Guangzhou, China, which may limit the external validity and applicability of our model to other regions and populations.”
The corrected sentence appears below:
“Our study also has some limitations and directions for future research. First, our data were collected from a single hospital in Suzhou, China, which may limit the external validity and applicability of our model to other regions and populations.”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: COVID-19, LASSO method, machine learning, clinical data, intelligent diagnosis
Citation: Jiang Z, Yang A, Chen H, Shi Y and Li X (2024) Corrigendum: Intelligent diagnosis of the severity of disease conditions in COVID-19 patients based on the LASSO method. Front. Public Health 12:1421217. doi: 10.3389/fpubh.2024.1421217
Received: 22 April 2024; Accepted: 23 April 2024;
Published: 06 May 2024.
Copyright © 2024 Jiang, Yang, Chen, Shi and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Aixiang Yang, yanygaixiang@163.com
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