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The patients\u2019medical history, demographic, epidemiologic and clinical data were collected from the electronic medical records system and paper based medical records, entered and managed by the Clinical Study Coordinators using the REDCap electronic data capture tool patient chart. The dataset was used to train and to evaluate predictive ML models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We overall trained, analysed and evaluated 19 predictive models (both supervised and unsupervised) on data from 824 patients described by 43 features. We focused our attention on models that provide an explanation that is understandable and directly usable by domain experts, and compared the results against other classical machine learning approaches. Among the former, JRIP showed the best performance in 10-fold cross validation, and the best average performance in a further validation test using a different patient dataset from the beginning of the third COVID-19 wave. Moreover, JRIP showed comparable performances with other approaches that do not provide a clear and\/or understandable explanation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The ML supervised models showed to correctly discern between low-risk and high-risk patients, even when the medical disease context is complex and the list of features is limited to information available at admission time. Furthermore, the models demonstrated to reasonably perform on a dataset from the third COVID-19 wave that was not used in the training phase. Overall, these results are remarkable: (i) from a medical point of view, these models evaluate good predictions despite the possible differences entitled with different care protocols and the possible influence of other viral variants (i.e. delta variant); (ii) from the organizational point of view, they could be used to optimize the management of health-care path at the admission time.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-022-02076-1","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T16:03:20Z","timestamp":1672243400000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2"],"prefix":"10.1186","volume":"22","author":[{"given":"Alessio","family":"Bottrighi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marzio","family":"Pennisi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Annalisa","family":"Roveta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Costanza","family":"Massarino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonella","family":"Cassinari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marta","family":"Betti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatiana","family":"Bolgeo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marinella","family":"Bertolotti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emanuele","family":"Rava","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Maconi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"2076_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/J.INFSOF.2020.106368","volume":"127","author":"LE Lwakatare","year":"2020","unstructured":"Lwakatare LE, Raj A, Crnkovic I, Bosch J, Olsson HH. 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All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. The \u201cComitato Etico Interaziendale dell\u2019Azienda Ospedaliera di Alessandria\u201d ethical committee approval was obtained on July 7, 2020, code being ASO.IRFI.20.03. All data was pseudonymed according to clinical study and data protection regulations. In strict accordance with the \u201cDecreto Legislativo June 30, 2003, n. 196\u201d and \u201cGeneral Data Protection Regulation n. 2016\/679\u201d laws the clinicians submitted an informed consent from their patients.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for pubblication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"340"}}