Minimizing Selection and Classification Biases. Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing"

Francisco Martos Pérez; Ricardo Gomez Huelgas; María Dolores Martín Escalante; José Manuel Casas Rojo

doi:10.2196/27142

Minimizing Selection and Classification Biases. Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing"

J Med Internet Res. 2021 May 26;23(5):e27142. doi: 10.2196/27142.

Authors

Francisco Martos Pérez^#¹, Ricardo Gomez Huelgas², María Dolores Martín Escalante¹, José Manuel Casas Rojo³

Affiliations

¹ Department of Internal Medicine, Hospital Costa del Sol, Marbella, Spain.
² Internal Medicine Department, Hospital Regional de Málaga, Málaga, Spain.
³ Internal Medicine Department, Infanta Cristina University Hospital, Parla (Madrid), Spain.

^# Contributed equally.

PMID: 33989163
PMCID: PMC8190647
DOI: 10.2196/27142

No abstract available

Keywords: COVID-19; SARS-CoV-2; artificial intelligence; big data; classification bias; critical care; electronic health records; predictive model; prognosis; tachypnea.

Publication types

Comment

MeSH terms

Bias
COVID-19*
Humans
Intensive Care Units
Machine Learning
Natural Language Processing*
Prognosis
Retrospective Studies
SARS-CoV-2