{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T11:21:32Z","timestamp":1770463292832,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T00:00:00Z","timestamp":1645488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Background: After the acute disease, convalescent coronavirus disease 2019 (COVID-19) patients may experience several persistent manifestations that require multidisciplinary pulmonary rehabilitation (PR). By using a machine learning (ML) approach, we aimed to evaluate the clinical characteristics predicting the effectiveness of PR, expressed by an improved performance at the 6-min walking test (6MWT). Methods: Convalescent COVID-19 patients referring to a Pulmonary Rehabilitation Unit were consecutively screened. The 6MWT performance was partitioned into three classes, corresponding to different degrees of improvement (low, medium, and high) following PR. A multiclass supervised classification learning was performed with random forest (RF), adaptive boosting (ADA-B), and gradient boosting (GB), as well as tree-based and k-nearest neighbors (KNN) as instance-based algorithms. Results: To train and validate our model, we included 189 convalescent COVID-19 patients (74.1% males, mean age 59.7 years). RF obtained the best results in terms of accuracy (83.7%), sensitivity (84.0%), and area under the ROC curve (94.5%), while ADA-B reached the highest specificity (92.7%). Conclusions: Our model enables a good performance in predicting the rehabilitation outcome in convalescent COVID-19 patients.<\/jats:p>","DOI":"10.3390\/jpm12030328","type":"journal-article","created":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T22:34:30Z","timestamp":1645569270000},"page":"328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Machine Learning Approach to Predict the Rehabilitation Outcome in Convalescent COVID-19 Patients"],"prefix":"10.3390","volume":"12","author":[{"given":"Sarah","family":"Adamo","sequence":"first","affiliation":[{"name":"Istituti Clinici Scientifici Maugeri IRCCS, Bioengineering Unit of Telese Terme Institute, 82037 Telese Terme, Italy"},{"name":"Department of Information Technology and Electrical Engineering, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9398-0428","authenticated-orcid":false,"given":"Pasquale","family":"Ambrosino","sequence":"additional","affiliation":[{"name":"Istituti Clinici Scientifici Maugeri IRCCS, Cardiac Rehabilitation Unit of Telese Terme Institute, 82037 Telese Terme, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7290-6432","authenticated-orcid":false,"given":"Carlo","family":"Ricciardi","sequence":"additional","affiliation":[{"name":"Istituti Clinici Scientifici Maugeri IRCCS, Bioengineering Unit of Telese Terme Institute, 82037 Telese Terme, Italy"},{"name":"Department of Information Technology and Electrical Engineering, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariasofia","family":"Accardo","sequence":"additional","affiliation":[{"name":"Istituti Clinici Scientifici Maugeri IRCCS, Pulmonary Rehabilitation Unit of Telese Terme Institute, 82037 Telese Terme, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Mosella","sequence":"additional","affiliation":[{"name":"Istituti Clinici Scientifici Maugeri IRCCS, Pulmonary Rehabilitation Unit of Telese Terme Institute, 82037 Telese Terme, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Cesarelli","sequence":"additional","affiliation":[{"name":"Istituti Clinici Scientifici Maugeri IRCCS, Bioengineering Unit of Telese Terme Institute, 82037 Telese Terme, Italy"},{"name":"Department of Information Technology and Electrical Engineering, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanni","family":"d\u2019Addio","sequence":"additional","affiliation":[{"name":"Istituti Clinici Scientifici Maugeri IRCCS, Bioengineering Unit of Telese Terme Institute, 82037 Telese Terme, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6751-9921","authenticated-orcid":false,"given":"Mauro","family":"Maniscalco","sequence":"additional","affiliation":[{"name":"Istituti Clinici Scientifici Maugeri IRCCS, Pulmonary Rehabilitation Unit of Telese Terme Institute, 82037 Telese Terme, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"Huang","year":"2020","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kamal, M., Abo Omirah, M., Hussein, A., and Saeed, H. 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