SYSTEM AND METHODS FOR GENERATING PREDICTIVE COMBINATIONS OF HOSPITAL MONITOR ALARMS

• US 20140292517A1
• Filed: 03/28/2014
• Published: 10/02/2014
• Est. Priority Date: 10/13/2011
• Status: Active Grant

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Hu, Xiao, Martin, Neil A.
• Granted Patent: US 9,600,990 B2
• Time in Patent Office: Days
• US Class Current: 340/573.1
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/7289 Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition
• CPC Class Codes: A61B 5/7292 Prospective gating, i.e. predicting the occurrence of a physiological event for use as a synchronisation signal
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G08B 21/02 Alarms for ensuring the safety of persons
• CPC Class Codes: G08B 21/0453 worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G08B 25/14 Central alarm receiver or annunciator arrangements
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Systems and methods are disclosed for monitoring data associated with a plurality of physiological characteristics of a patient, comprising: the methods include generating a set of super-alarm patterns associated with the plurality of physiological conditions, wherein the super-alarm patterns comprising data relating to a combination of at least two individual raw alarms from independent physiological data streams that co-occur within a temporal window, and triggering an alarm if a combination of the input physiological data matches at least a portion of a generated super-alarm pattern.

Claims

1. A system for monitoring data associated with a plurality of physiological characteristics of a patient, comprising:

a processor configured for receiving input from a plurality of independent physiological data streams from one or more monitoring devices;

wherein each of the physiological data streams comprises an individual raw alarm associated with the physiological data within the stream; and

programming executable on the processor for;

reading the input physiological data streams;

comparing the input data stream against a set of super-alarm patterns associated with the plurality of physiological conditions;

the super-alarm patterns comprising data relating to a combination of at least two individual raw alarms from independent physiological data streams that co-occur within a temporal window; and

triggering an alarm if a combination of the input physiological data matches at least a portion of a generated super-alarm pattern.

2. A system as recited in claim 1, wherein the triggered alarm is predictive of an adverse event associated with the physiological characteristics of a patient.

3. A system as recited in claim 2, wherein the triggered alarm is predictive of a code-blue event.

4. A system as recited in claim 2, wherein the set of super-alarm patterns are generated by:

acquiring physiological monitoring case data from a comprising raw alarm data plurality of patient cases; and

detecting super-alarm patterns occurring above a specified frequency within a window of time directly preceding an adverse event within each of the cases.

5. A system as recited in claim 4, wherein the set of super-alarm patterns are generated by:

acquiring physiological monitoring control data comprising raw alarm data from a plurality of control patients that are not coded; and

discretizing a value field for raw alarms based on the case data and the control data.

6. A system as recited in claim 5, wherein raw control alarms and case alarms that co-occur within a specified time window are pre-processed to generate an output of an array of alarm codes, each of which identifies an individual raw alarm.

7. A system as recited in claim 5, wherein discretizing a value field comprises dividing an alarm with a value code into sub-codes.

8. A system as recited in claim 5, wherein the control data is used to filter out super-alarm patterns identified for adverse event patients that have also occurred above a specified frequently for control patients.

9. A system as recited in claim 5:

wherein a super-alarm pattern is excluded from inclusion within the super-alarm pattern set if the super-alarm pattern has false positive rate (FPR) is greater than a predetermined threshold; and

wherein the false positive rate (FPR) of the super-alarm pattern is a function of the acquired control data.

10. A system as recited in claim 5, wherein the set of super-alarm patterns are generated by:

generating encoding schema;

wherein encoding schema used to encode alarm codes for case alarms and control alarms.

11. A system as recited in claim 10, further comprising:

generating encoding schema as a function of an occurrence frequency for each alarm code.

12. A system as recited in claim 2, wherein the set of super-alarm patterns are generated by:

applying closed-set filtering to remove super-alarm patterns that are included as a subset of a larger super-alarm pattern.

13. A system for generating a set of alarm patterns associated with a plurality of physiological characteristics of a patient, the alarm patterns being predictive of an adverse event associated with the physiological characteristics of a patient, comprising:

a database comprising physiological monitoring case data comprising raw alarm data from a plurality of patient cases and a plurality of control patients that are not coded;

a processor;

programming executable on the processor for;

evaluating raw alarm data from a plurality of patient cases and a plurality of control patients; and

generating a set of super-alarm patterns associated with the plurality of physiological conditions;

the super-alarm patterns comprising data relating to a combination of at least two individual raw alarms from independent physiological data readings of the patient.

14. A system as recited in claim 13, wherein generating a set of super-alarm patterns comprises:

detecting super-alarm patterns occurring above a specified frequency within a window of time directly preceding an adverse event within each of the cases.

15. A system as recited in claim 13, wherein generating a set of super-alarm patterns comprises:

discretizing a value field for raw alarms based on the case data and the control data.

16. A system as recited in claim 13, wherein raw control alarms and case alarms that co-occur within a specified time window are pre-processed to generate an output of an array of alarm codes, each of which identifies an individual raw alarm.

17. A system as recited in claim 15, wherein discretizing a value field comprises dividing an alarm with a value code into sub-codes.

18. A system as recited in claim 13, wherein the control data is used to filter out super-alarm patterns identified for adverse event patients that have also occurred above a specified frequently for control patients.

19. A system as recited in claim 13:

wherein a super-alarm pattern is excluded from inclusion within the super-alarm pattern set if the super-alarm pattern has a false positive rate (FPR) greater than a predetermined threshold;

wherein the false positive rate (FPR) of the super-alarm pattern is a function of the acquired control data.

20. A system as recited in claim 13, wherein generating a set of super-alarm patterns further comprises:

generating encoding schema;

wherein encoding schema is used to encode alarm codes for case alarms and control alarms.

21. A system as recited in claim 20, further comprising:

generating encoding schema as a function of an occurrence frequency for each alarm code.

22. A system as recited in claim 13, wherein generating a set of super-alarm patterns further comprises:

applying closed-set filtering to remove super-alarm patterns that are included as a subset of a larger super-alarm pattern.

23. A monitor for predicting an adverse event associated with the physiological characteristics of a patient, comprising:

one or more monitor inputs configured for receiving input from a plurality of independent physiological data streams generated from one or more monitoring devices;

wherein each of the physiological data streams comprises an individual raw alarm associated with the physiological data within the stream;

a processor coupled to the one or more monitor inputs; and

programming executable on the processor for;

reading the input physiological data streams;

comparing the input data stream against a set of super-alarm patterns associated with the plurality of physiological conditions;

the super-alarm patterns comprising data relating to a combination of at least two individual raw alarms from independent physiological data streams that co-occur within a temporal window; and

triggering an alarm if a combination of the input physiological data matches at least a portion of a generated super-alarm pattern

24. A monitor as recited in claim 23, wherein the triggered alarm is predictive of a code-blue event.

25. A monitor as recited in claim 23, wherein the set of super-alarm patterns are generated by:

acquiring physiological monitoring case data from a comprising raw alarm data plurality of patient cases;

detecting super-alarm patterns occurring within a window of time directly preceding an adverse event within each of the cases.

26. A monitor as recited in claim 25, wherein the set of super-alarm patterns are generated by:

acquiring physiological monitoring control data comprising raw alarm data from a plurality of control patients that are not coded; and

discretizing a value field for raw alarms based on the case data and the control data.

27. A monitor as recited in claim 26, wherein raw control alarms and case alarms that co-occur within a specified time window are pre-processed to generate an output of an array of alarm codes, each of which identifies an individual raw alarm.

28. A monitor as recited in claim 26, wherein discretizing a value field comprises dividing an alarm with a value code into sub-codes.

29. A monitor as recited in claim 26, wherein the control data is used to filter out super-alarm patterns identified for adverse event patients that have also occurred above a specified frequently for control patients.

30. A monitor as recited in claim 26:

wherein a super-alarm pattern is excluded from inclusion within the super-alarm pattern set if the super-alarm pattern has a false positive rate (FPR) greater than a predetermined threshold;

wherein the false positive rate (FPR) of the super-alarm pattern is a function of the acquired control data.

31. A monitor as recited in claim 26, wherein the set of super-alarm patterns are generated by:

generating encoding schema;

wherein encoding schema used to encode alarm codes for case alarms and control alarms.

32. A monitor as recited in claim 31, further comprising:

generating encoding schema as a function of an occurrence frequency for each alarm code.

33. A monitor as recited in claim 23, wherein the set of super-alarm patterns are generated by:

applying closed-set filtering to remove super-alarm patterns that are included as a subset of a larger super-alarm pattern.