RT Journal Article
T1 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".
A1 Martos-Perez, Francisco
A1 Gómez-Huelgas, Ricardo
A1 Martin-Escalante, Maria Dolores
A1 Casas-Rojo, Jose Manuel
K1 COVID-19
K1 SARS-CoV-2
K1 Artificial intelligence
K1 Big data
K1 Classification bias
K1 Critical care
K1 Electronic health records
K1 Predictive model
K1 Prognosis
K1 Tachypnea
AB The paper by Izquierdo et al [1], published in the recent issue of the Journal of Medical Internet Research, employed a combination of conventional and machine learning tools to describe the clinical characteristics of patients with COVID-19 and the factors that predict intensive care unit (ICU) admission. We would like to make some comments about its design. The authors should have provided the proportion of patients with a positive microbiological diagnosis. If the artificial intelligence software’s capacity for retrieving this information is limited in some way, this should be explained. The classification bias introduced by the lack of microbiological confirmation may have been significant since the study includes patients from January 1, 2020. Although some undiagnosed cases have likely been present prior to the first declared case (March 1, 2020) [2] in Castilla-La Mancha, it is improbable that there were many of them.
PB JMIR Publications
YR 2021
FD 2021-05-26
LK http://hdl.handle.net/10668/17792
UL http://hdl.handle.net/10668/17792
LA en
NO Martos Pérez F, Gomez Huelgas R, Martín Escalante MD, Casas Rojo JM. 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
DS RISalud
RD Sep 4, 2025