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Introduction: Objective: To create a valuable practical tool for evaluating the risk of LC development. Material and methods: 1150 patients from the Polish STOP-COVID registry (PoLoCOV study) were used to develop the risk score. The patients were ill between 03/2020 and 04/2022. To develop a clinically useful scoring model. The LC risk score was generated using the machine learning-based framework AutoScore. Patient data were first randomised into a training (70% of output) and a test (30% of output) cohorts. Due to relatively small study group, cross-validation was used. Model predictive ability was evaluated based on the ROC curve and the AUC value. The result of the risk score for a given patient was the total value of points assigned to selected variables. Results: To create long COVID Risk Score, eight variables were ultimately selected due to their significance and clinical value. Female gender …