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Editor-in-chief
Maria Stella Graziani

Deputy Director
Martina Zaninotto

Associate Editors
Ferruccio Ceriotti
Davide Giavarina
Bruna Lo Sasso
Giampaolo Merlini
Martina Montagnana
Andrea Mosca
Paola Pezzati
Rossella Tomaiuolo
Matteo Vidali

EIC Assistant
Francesco Busardò

International Advisory Board Khosrow Adeli Canada
Sergio Bernardini Italy
Marcello Ciaccio Italy
Eleftherios Diamandis Canada
Philippe Gillery France
Kjell Grankvist Sweden
Hans Jacobs The Netherlands
Eric Kilpatrick UK
Magdalena Krintus Poland
Giuseppe Lippi Italy
Mario Plebani Italy
Sverre Sandberg Norway
Ana-Maria Simundic Croatia
Tommaso Trenti Italy
Cas Weykamp The Netherlands
Maria Willrich USA
Paul Yip Canada


Publisher
Biomedia srl
Via L. Temolo 4, 20126 Milano

Responsible Editor
Giuseppe Agosta

Editorial Secretary
Chiara Riva
Biomedia srl
Via L. Temolo 4, 20126 Milano
Tel. 0245498282
email: biochimica.clinica@sibioc.it

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ISSN print: 0393 – 0564
ISSN digital: 0392- 7091



Articoli con TAG: machine learning

Identificazione di positività al SARS-CoV-2 attraverso metodi di Machine Learning sui dati dell’esame emocromocitometrico: validazione esterna di modelli allo stato dell'arte
Identification of SARS-CoV-2 positivity using machine learning methods on blood count data: external validation of state-of-the-art models.
<p>Introduction: The aim of the paper is to present the results from the process of external validation of a number of machine learning (ML) models that had been previously developed to detect SARS-CoV-2 virus positivity on both symptomatic and asymptomatic patients on the basis of the complete blood count (CBC) test.<br />Methods: Briefly, models were trained using a dataset of 816 COVID-19 positive and 920 negative cases collected at the emergency departments of IRCCS Hospital San Raffaele and IRCCS Istituto Ortopedico Galeazzi. 21 parameters, including the results of the CBC analysis, age [60.9 (0.9) years], gender (57% males) and the presence of COVID-19 related symptoms were used. The validation regarded the evaluation of the error rate (through different metrics, including accuracy, sensitivity, specificity and the area under the curve (AUC)) of the models considered. This external validation was conducted on two well balanced datasets coming from two different hospitals in Northern Italy: Desio hospital and Bergamo Papa Giovanni XXIII hospital. 163 positive and 174 true negative patients from Desio, and 104 positive and 145 true negative from Bergamo were included in the validation.<br />Results: The performance of the predictive models is satisfactory as we can report an average AUC of 95% for both external datasets.<br />Conclusion: ML models have been applied to hematological parameters for a more rapid and cost-effective detection of the COVID-19 disease. We make the point that validated models may be useful in the management and early detection of potential COVID-19 patients.</p>
Biochimica Clinica ; 45(3) 281-289
Contributi Scientifici - Scientific Papers
 
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
<p>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).<br />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.</p>
Biochimica Clinica ; 45(4) 348-364
Rassegne - Reviews