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Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

Cabitza, Federico; Campagner, Andrea; Ferrari, Davide; Di Resta, Chiara; Ceriotti, Daniele; Sabetta, Eleonora; Colombini, Alessandra; De Vecchi, Elena; Banfi, Giuseppe; Locatelli, Massimo; Carobene, Anna


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    <dct:title>Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-10-12</dct:issued>
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    <dct:description>&lt;p&gt;The .xlsx dataset includes all patients used for training, internal-external and external validation: these can be distinguished by looking at the ID (first column) in the dataset: those in format Axxxx-&amp;lt;Date&amp;gt; are the data used for the training, those in the format 20xx are the data used for the internal-external validation, while the remaining data were used for external validation.&lt;/p&gt; &lt;p&gt;As regards the features: for the Target feature the value 1 stands for &amp;quot;Positive to COVID-19&amp;quot; while the value 0 stands for &amp;quot;Negative to COVID-19&amp;quot;; while for the Sex feature the value 1 stands for &amp;quot;Male&amp;quot; while the value 0 stands for &amp;quot;Female&amp;quot;.&lt;/p&gt; &lt;p&gt;The full article is available at: https://www.degruyter.com/view/journals/cclm/ahead-of-print/article-10.1515-cclm-2020-1294/article-10.1515-cclm-2020-1294.xml.&lt;/p&gt; &lt;p&gt;A pre-print version of the article is also available on MedrXiv:&amp;nbsp;https://www.medrxiv.org/content/10.1101/2020.10.02.20205070v1&lt;/p&gt; &lt;p&gt;&lt;strong&gt;ABSTRACT&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Background&lt;/strong&gt; The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19),&amp;nbsp;presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative&amp;nbsp;rates around 15&amp;ndash;20%, and expensive equipment. The hematochemical values of routine blood exams could&amp;nbsp;represent a faster and less expensive alternative.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Methods&lt;/strong&gt; Three different training data set of hematochemical values from 1,624 patients (52% COVID-19&amp;nbsp;positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine&amp;nbsp;learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical,&amp;nbsp;coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub&amp;nbsp;datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive)&amp;nbsp;from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Results&lt;/strong&gt; We developed five ML models: for the complete OSR dataset, the area under the receiver operating&amp;nbsp;characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 15 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC 16 from 0.75 to 0.78; and specificity from 0.92 to 0.96.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Conclusions&lt;/strong&gt; ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast&amp;nbsp;and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries,&amp;nbsp;or in countries facing an increase in contagions.&lt;/p&gt;</dct:description>
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