A computer-implemented method of predicting the occurrence of a critical alarm for a subject undergoing physiological parameter monitoring, the computer-implemented method comprising: receiving a non-critical alarm signal indicating that the subject has entered a clinically undesirable state at a time of occurrence; and in response to the non-critical alarm signal: obtaining a set of two or more pre-alarm values derived from and/or comprising two or more values of at least one monitored physiological parameter (34A-34F) of the subject collected within a pre-alarm time window (TW 1) of a first predetermined length, wherein the start time (t tw) of the pre-alarm time window depends upon the time of occurrence (t o) of the non-critical alarm signal and wherein the pre-alarm time window ends before or at the time of occurrence of the non-critical alarm signal; and processing, using a machine-learning algorithm, the set of two or more pre-alarm values to generate a predictive indicator indicating a probability that the non-critical alarm signal will be followed, within a post-alarm time window (TW 2), by a critical alarm signal indicating that the subject has entered a clinically actionable state, wherein the post-alarm time window is of a second predetermined length and begins at the time of occurrence of the non-critical alarm signal. receiving a non-critical alarm signal indicating that the subject has entered a clinically undesirable state at a time of occurrence; and in response to the non-critical alarm signal: obtaining a set of two or more pre-alarm values derived from and/or comprising two or more values of at least one monitored physiological parameter (34A-34F) of the subject collected within a pre-alarm time window (TW 1) of a first predetermined length, wherein the start time (t tw) of the pre-alarm time window depends upon the time of occurrence (t o) of the non-critical alarm signal and wherein the pre-alarm time window ends before or at the time of occurrence of the non-critical alarm signal; and processing, using a machine-learning algorithm, the set of two or more pre-alarm values to generate a predictive indicator indicating a probability that the non-critical alarm signal will be followed, within a post-alarm time window (TW 2), by a critical alarm signal indicating that the subject has entered a clinically actionable state, obtaining a set of two or more pre-alarm values derived from and/or comprising two or more values of at least one monitored physiological parameter (34A-34F) of the subject collected within a pre-alarm time window (TW 1) of a first predetermined length, wherein the start time (t tw) of the pre-alarm time window depends upon the time of occurrence (t o) of the non-critical alarm signal and wherein the pre-alarm time window ends before or at the time of occurrence of the non-critical alarm signal; and processing, using a machine-learning algorithm, the set of two or more pre-alarm values to generate a predictive indicator indicating a probability that the non-critical alarm signal will be followed, within a post-alarm time window (TW 2), by a critical alarm signal indicating that the subject has entered a clinically actionable state, wherein the post-alarm time window is of a second predetermined length and begins at the time of occurrence of the non-critical alarm signal.