Predicting critical alarms
AssigneeKoninklijke Philips N.V.
InventorRohan JOSHI
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Abstract
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.
Claims15
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 one or more pre-alarm values derived from and/or comprising one or more values of at least one monitored physiological parameter of the subject collected within a pre-alarm time window (TW1) of a first predetermined length, wherein the start time (ttw) of the pre-alarm time window depends upon the time of occurrence (to) 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 one 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 (TW2), 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.
A system for predicting the occurrence of a critical alarm for a subject undergoing physiological parameter monitoring, the system comprising:
- an alarm receiving module adapted to receive a non-critical alarm signal indicating that the subject has entered a clinically undesirable state;
- and
- an alarm predicting module adapted to, in response to the non-critical alarm:
- obtain a set of one or more pre-alarm values derived from and/or comprising values of at least one monitored physiological parameter of the subject collected within a pre-alarm time window (TW1) of a first predetermined length, wherein the start time (ttw) of the pre-alarm time window depends upon the time of occurrence (to) 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
- process, using a machine-learning algorithm, the set of one 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 (TW2), 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.
Dependencies are inferred from the claim text.