Martos-Perez, FranciscoGómez-Huelgas, RicardoMartin-Escalante, Maria DoloresCasas-Rojo, Jose Manuel2023-02-092023-02-092021-05-26Martos 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):e27142http://hdl.handle.net/10668/17792The 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/COVID-19SARS-CoV-2Artificial intelligenceBig dataClassification biasCritical careElectronic health recordsPredictive modelPrognosisTachypneaBiasCOVID-19HumansIntensive Care UnitsMachine LearningNatural Language ProcessingPrognosisRetrospective StudiesSARS-CoV-2research article33989163open accessAprendizaje automáticoUnidades de Cuidados IntensivosDiagnósticoInteligencia ArtificialProgramas informáticos10.2196/271421438-8871PMC8190647https://www.jmir.org/2021/5/e27142/PDFhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190647/pdf