The retrospective study will be used to develop an artificial intelligence model of risk stratification of physiological and psychological complications arising from the information available in the electronic medical record and first consultation report to support patients and healthcare professionals in better managing the healthcare process for patients diagnosed with long COVID.
The stratification of the risk of complications related to persistent COVID both physiological and psychological in a personalized way would optimize the cost-effectiveness model for the management of these patients. Similarly, the early detection of complications associated with persistent COVID in patients belonging to vulnerable groups would improve care times and, therefore, the patient's prognosis. The primary objective for this study is to gather anonymized retrospective data of patients suffering from long COVID in order to contribute to the generation of the SENSING-AI cohort.
Study Type
OBSERVATIONAL
Enrollment
103
There will be a review of available clinical data sources related to use cases. In addition, this information will be complemented by a cohort of anonymized retrospective data of 100 cases obtained from the clinical information resulting from the assistance to COVID-19 patients managed by the Primary Care Health District of Sevilla Norte and the Infectious Diseases Department of the Virgen Macarena University Hospital
Virgen Macarena University Hospital
Seville, Seville, Spain
Retrospective SENSING-AI cohort
The retrospective SENSING-AI cohort will be fed from clinical information of 100 cases of patients with long COVID-19.
Time frame: 1 month
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