In the intensive care unit, patients with crucial, life-threatening conditions are admitted and need constant monitoring. Here, the need for a quick and efficient decision support tool is the greatest. The use of machine learning has shown promising results in identifying patients at risk of different severe conditions in the intensive care unit and detection at an early stage is crucial in order to take preventive measures. This especially applies to conditions that can be hard to manage once developed, such as circulatory shock. In this master’s thesis, a machine learning modeling approach is suggested to detect and differentiate the onset of three types of circulatory shock – cardiogenic, hypovolemic and septic shock. Data was used from the open-source database MIMIC which represents thousands of patients from intensive care. The data was preprocessed and labels for the three shock types were created using ICD-9 codes combined with a proxy that is closely related to the condition – vasopressor. Different machine learning algorithms were then used for a static onset prediction as a base. The best performing models were also trained for a dynamic onset prediction in order to make predictions up to four hours ahead of onset. All models were evaluated using different evaluation metrics and at last, an interpretation method was used to enable a simpler interpretation of the results. The final results show that it is possible to detect and distinguish between the three types of shock, up to four hours ahead of onset. For future developments, further development and validation using more data should be the main focus before testing it in a clinical setting.