{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:12:12Z","timestamp":1760145132084,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"HORIZON 2020 Programme of the European Commission-H2020-SC1-PHE-CORONAVIRUS-2020-2-CNECT","award":["101016065"],"award-info":[{"award-number":["101016065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Apart from providing user-friendly applications that support digitized healthcare routines, the use of wearable devices has proven to increase the independence of patients in a healthcare setting. By applying machine learning techniques to real health-related data, important conclusions can be drawn for unsolved issues related to disease prognosis. In this paper, various machine learning techniques are examined and analyzed for the provision of personalized care to COVID-19 patients with mild symptoms based on individual characteristics and the comorbidities they have, while the connection between the stimuli and predictive results are utilized for the evaluation of the system\u2019s transparency. The results, jointly analyzing wearable and electronic health record data for the prediction of a daily dyspnea grade and the duration of fever, are promising in terms of evaluation metrics even in a specified stratum of patients. The interpretability scheme provides useful insight concerning factors that greatly influenced the results. Moreover, it is demonstrated that the use of wearable devices for remote monitoring through cloud platforms is feasible while providing awareness of a patient\u2019s condition, leading to the early detection of undesired changes and reduced visits for patient screening.<\/jats:p>","DOI":"10.3390\/make6020062","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T12:27:33Z","timestamp":1718972853000},"page":"1323-1342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Extracting Interpretable Knowledge from the Remote Monitoring of COVID-19 Patients"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2171-1069","authenticated-orcid":false,"given":"Melina","family":"Tziomaka","sequence":"first","affiliation":[{"name":"Department of Digital Systems, University of Piraeus, 185 34 Piraeus, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9234-0069","authenticated-orcid":false,"given":"Athanasios","family":"Kallipolitis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Piraeus, 185 34 Piraeus, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4510-5522","authenticated-orcid":false,"given":"Andreas","family":"Menychtas","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Piraeus, 185 34 Piraeus, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8630-7200","authenticated-orcid":false,"given":"Parisis","family":"Gallos","sequence":"additional","affiliation":[{"name":"BioAssist SA, 265 04 Rio, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9282-0919","authenticated-orcid":false,"given":"Christos","family":"Panagopoulos","sequence":"additional","affiliation":[{"name":"BioAssist SA, 265 04 Rio, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4984-0476","authenticated-orcid":false,"given":"Alice Georgia","family":"Vassiliou","sequence":"additional","affiliation":[{"name":"1st Department of Critical Care Medicine and Pulmonary Services, School of Medicine, National and Kapodistrian University of Athens, Evangelismos Hospital, 106 76 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7306-713X","authenticated-orcid":false,"given":"Edison","family":"Jahaj","sequence":"additional","affiliation":[{"name":"1st Department of Critical Care Medicine and Pulmonary Services, School of Medicine, National and Kapodistrian University of Athens, Evangelismos Hospital, 106 76 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2219-7292","authenticated-orcid":false,"given":"Ioanna","family":"Dimopoulou","sequence":"additional","affiliation":[{"name":"1st Department of Critical Care Medicine and Pulmonary Services, School of Medicine, National and Kapodistrian University of Athens, Evangelismos Hospital, 106 76 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2508-500X","authenticated-orcid":false,"given":"Anastasia","family":"Kotanidou","sequence":"additional","affiliation":[{"name":"1st Department of Critical Care Medicine and Pulmonary Services, School of Medicine, National and Kapodistrian University of Athens, Evangelismos Hospital, 106 76 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2860-399X","authenticated-orcid":false,"given":"Ilias","family":"Maglogiannis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Piraeus, 185 34 Piraeus, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101706","DOI":"10.1016\/j.artmed.2019.101706","article-title":"The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetes","volume":"100","author":"Sowa","year":"2019","journal-title":"Artif. 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