Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression.

Background And Objectives: Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease severity changes through the course of the disease. In that way, predicting disease progression from mild towards moderate, severe and critical condition, would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not necessary.

Methods: We present a methodology for the classification of patients into 4 distinct categories of the clinical condition of COVID-19 disease.

Giant liver hemangioma in patient with ileal gastrointestinal stromal tumor.

Introduction: Gastrointestinal stromal tumors are the most common mesenchymal neoplasms of the gastrointestinal tract. These tumors represent more than 80% of all mesenchymal tumors found in the gastrointestinal tract, though they account for only approximately 3% of all gastrointestinal malignancies. Literature offers case reports, which describe symptomatic gastrointestinal stromal tumors and they generally represent patients with larger tumors.