English 
Italian Tecniche di apprendimento automatico basato sui risultati di esami di medicina di laboratorio nella diagnosi e prognosi per i pazienti COVID-19: una revisione sistematica
Machine Learning based on laboratory medicine test results in diagnosis and prognosis for COVID-19 patients: a systematic review
AUTORI
1IRCCS Istituto Scientifico Ospedale San Raffaele, Milano
2IRCCS Istituto Ortopedico Galeazzi, Milano
3Università Vita e Salute San Raffaele, Milano
4Azienda Ospedaliera Universitaria Senese - Dipartimento Biotecnologie Mediche, Università degli Studi di Siena
5Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milano
6DISCo, Università degli Studi di Milano-Bicocca, Milano, Italia
ABSTRACT
Machine Learning based on laboratory medicine test results in diagnosis and prognosis for COVID-19 patients: a systematic review
The rapid detection of SARS-CoV-2 infections is essential for both diagnostic and prognostic reasons: however, the current gold standard for COVID-19 diagnosis, that is the rRT-PCR test, is affected by long turnaround time, potential shortage of reagents, high false-negative rates and high costs. Thus, Machine Learning (ML) based methods have recently attracted increasing interest, especially when applied to digital imaging (x-rays and CT scans).
In this article, we review the literature on ML-based diagnostic and prognostic methods grounding on hematochemical parameters. In doing so, we address the gap in the existing literature, which has so far neglected the application of ML to laboratory medicine. We surveyed 20 research articles, extracted from the Scopus and PubMed indexes. These studies were characterized by a large heterogeneity, in terms of considered laboratory and clinical parameters, sample size, reference population, employed ML methods and validation procedures. Most studies were found to be affected by reporting and replicability issues: among the surveyed studies, only three reported complete information regarding the analytic methods (units of measure, analyzing equipment), while nine studies reported no information at all. Furthermore, only six studies reported results on independent external validation. In light of these results, we discuss the importance of a tighter collaboration between data scientists and medicine laboratory professionals, so as to correctly characterize the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the correct interpretation of the results, and gain actual usefulness by applying ML methods in clinical practice.