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1. WO2020109381 - PRÉDICTION D'ALARMES CRITIQUES

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CLAIMS:

1. A computer-implemented method (1) of predicting the occurrence of a critical alarm for a subject undergoing physiological parameter monitoring, the computer-implemented method comprising:

receiving (10) a non-critical alarm signal (4) 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 (11) 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 (34A-34F) of the subject collected within a pre-alarm time window (TWi) of a first predetermined length, wherein the start time (Lw) of the pre-alarm time window depends upon the time of occurrence (t0) 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 (12), using a machine- learning algorithm, the set of one or more pre-alarm values to generate a predictive indicator (6) 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.

2. The computer-implemented method of claim 1, wherein the pre-alarm time window (TWi) ends at the time of occurrence (t0) of the non-critical alarm signal.

3. The computer-implemented method of claim 1 or 2, wherein the first predetermined length is greater than the second predetermined length.

4. The computer-implemented method of any of claims 1 to 3, wherein the first predetermined length is from 1 to 3 minutes, and the second predetermined length is from 1 to 3 minutes.

5. The computer- implemented method of any of claims 1 to 4, further comprising, in response to the non-critical alarm, obtaining information about any other alarm for the subject occurring during the pre-alarm time window,

wherein the step of processing the obtained values comprises processing at least the obtained set of one or more pre-alarm values and the information about any other alarm using the machine- learning algorithm to thereby generate the predictive indicator.

6. The computer-implemented method of any of claims 1 to 5, further comprising, in response to the non-critical alarm, obtaining metadata of the subject undergoing physiological parameter monitoring,

wherein the step of processing the obtained values comprises processing at least the obtained set of one or more pre-alarm values and the metadata of the subject using the machine- learning algorithm to thereby generate the predictive indicator.

7. The computer-implemented method of any of claims 1 to 6, further comprising, in response to the non-critical alarm, determining at least one correlation measure indicative of a correlation between two or more values of at least one monitored physiological parameter,

wherein the step of obtaining a set of one or more pre-alarm values comprises including the at least one correlation measure in the set of one or more pre-alarm values.

8. The computer-implemented method of any of claims 1 to 7, wherein:

the predictive indicator is a binary output indicating a prediction of whether or not the non-critical alarm will be followed by a critical alarm within the post-alarm time window; and

the machine- learning algorithm is configured to have a specificity of no less than 0.95.

9. The computer-implemented method of any of claims 1 to 8, further comprising, in response to the predictive indicator indicating that a likelihood that the non-critical alarm will develop into a critical alarm is at or above a predetermined threshold, generating a first clinician perceptible alert.

10. The computer-implemented method of claim 9, further comprising, in response to the predictive indicator indicating that a likelihood that the non-critical alarm will develop into a critical alarm is below a predetermined threshold, not generating the first clinician perceptible alert.

11. The computer-implemented method of any of claims 1 to 10, further comprising:

obtaining metadata of the subject undergoing physiological parameter monitoring; and

setting the first predetermined length based on the obtained metadata of the subject.

12. The computer-implemented method of any of claims 1 to 11, wherein the non-critical alarm signal indicates that at least one physiological parameter of the subject has entered a clinically undesirable state, the method further comprising, in response to the non-critical alarm:

obtaining one or more values for the at least one physiological parameter that triggers the non-critical alarm at a time the non-critical alarm is triggered;

modifying the first predetermined length based on the obtained one or more values for the at least one physiological parameter that triggers the non-critical alarm at a time the non-critical alarm is triggered.

13. A computer program comprising code means for implementing the method of any one of claims 1 to 12 when said program is run on a computer.

14. A system (60) for predicting the occurrence of a critical alarm for a subject undergoing physiological parameter monitoring, the system comprising:

an alarm receiving module (61) adapted to receive a non-critical alarm signal indicating that the subject has entered a clinically undesirable state; and

an alarm predicting module (63) 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 (34A-34F) of the subject collected within a pre-alarm time window (TWi) of a first predetermined length, wherein the

start time (hw) of the pre-alarm time window depends upon the time of occurrence (t0) 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.

15. The system of claim 14, further comprising a user interface (65) arranged to generate a clinician perceptible alert in response to the predictive indicator indicating that a likelihood that the non-critical alarm will develop into a critical alarm is above a predetermined threshold.