{"title":"ML-Based Framework to Predict the Severity of the Symptomatology in Patients with Post-Acute COVID-19 Syndrome.","authors":"Adina Nitulescu, Mihaela Crisan-Vida, Cristina Tudoran, Lacramioara Stoicu-Tivadar","doi":"10.3233/SHTI241071","DOIUrl":null,"url":null,"abstract":"<p><p>The paper describes a cohort of patients with post-acute COVID-19 syndrome, evaluated for the first time between week 3 and week 12 from the onset of symptoms following the acute COVID-19 infection. The patient's baseline clinical features were used as predictors. The analysis showed that older patients with comorbidities are at higher risk of developing more long-lasting post COVID-19 symptoms. Further integration with a personal monitoring device and combination with the Fast Healthcare Interoperability Resources extends the standardization, interoperability and possibility of integration and harmonization with other hospital systems. By employing advanced machine learning techniques, insights can be derived and further examined to improve the outcome and early treatment options for patients.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"99-103"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Studies in health technology and informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/SHTI241071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The paper describes a cohort of patients with post-acute COVID-19 syndrome, evaluated for the first time between week 3 and week 12 from the onset of symptoms following the acute COVID-19 infection. The patient's baseline clinical features were used as predictors. The analysis showed that older patients with comorbidities are at higher risk of developing more long-lasting post COVID-19 symptoms. Further integration with a personal monitoring device and combination with the Fast Healthcare Interoperability Resources extends the standardization, interoperability and possibility of integration and harmonization with other hospital systems. By employing advanced machine learning techniques, insights can be derived and further examined to improve the outcome and early treatment options for patients.
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