Embodiments propose methods and system for predicting the occurrence of critical alarms in response to the occurrence of less severe, non-critical alarms. It is proposed to use a machine-learning model trained to discern whether a non-critical alarm will be followed by a critical alarm within a particular time period, e.g. whether the non-critical alarm will develop into a critical alarm. Unlike existing alarm systems which are merely threshold based, this approach uses physiological data from a window of data. This window of data can be expected to carry more information than a simple breach of the threshold.