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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.
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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.
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103 participants in 1 patient group
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Data sourced from clinicaltrials.gov
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