SYSTEMS AND METHODS FOR DIAGNOSING THE CAUSE OF TREND SHIFTS IN HOME HEALTH DATA

• US 20090187082A1
• Filed: 01/21/2008
• Published: 07/23/2009
• Est. Priority Date: 01/21/2008
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Osborn, Mark D., Cuddihy, Paul E.
• Application Number:
  US12/017,185
• Publication Number:
  US 20090187082A1
• Time in Patent Office: Days
• US Class Current: 600/300
• 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 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• 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: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

A system and method for determining the cause of a trend shift in physiological data received from a patient under observation includes receiving physiological data on a plurality of measured physiological parameters from the patient and performing a statistical analysis on a portion of the physiological data to determine a measured shift over a confidence interval in each of the plurality of physiological parameters. A signature shift is defined for each of the plurality of physiological parameters that is indicative of a pre-determined medical condition and the measured shift confidence interval of each of the plurality of physiological parameters is compared to these signature shifts. From this comparison between the measured shift confidence interval and the signature shift of each of the plurality of physiological parameters, a physiological assessment is formulated.

Claims

1. An automated method for diagnosing the cause of a trend shift in physiological data including the steps of:

receiving physiological data from a patient under observation, the physiological data comprising data on a plurality of measured physiological parameters;

performing a statistical analysis on a portion of the physiological data to determine a measured shift confidence interval in each of the plurality of physiological parameters;

defining a signature shift for each of the plurality of physiological parameters, the signature shifts for the plurality of physiological parameters indicative of a pre-determined medical condition;

comparing the measured shift confidence interval of each of the plurality of physiological parameters to the signature shift associated with each of the plurality of physiological parameters; and

detecting a change in patient condition based on the comparison between the measured shift confidence interval and the signature shift of each of the plurality of physiological parameters.

2. The method of claim 1 wherein the step of defining a signature shift includes selecting a fuzzy model comprised of a plurality of rules, the plurality of rules associating a shift in one of the plurality of physiological parameters to a shift in at least one other physiological parameter in the plurality of physiological parameters to define a pre-determined physiological condition.

3. The method of claim 2 wherein the step of comparing includes comparing the measured shift confidence interval of each of the plurality of physiological parameters to each of the plurality of rules.

4. The method of claim 2 wherein the step of detecting a change in patient condition includes determining if one of the plurality of rules matches the measured shift confidence intervals, wherein a rule matches if the measured shift confidence intervals of the physiological parameters match the signature shift for each physiological parameter.

5. The method of claim 4 further comprising the step of determining a confidence level in a match between the signature shifts and the measured shift confidence intervals.

6. The method of claim 5 wherein the step of determining a confidence level comprises providing an evaluation function that determines how well each of the plurality of rules and the signature shifts associated with each of those rules matches the measured shift confidence intervals.

7. The method of claim 5 further comprising the step of generating an alert when the confidence level meets a predetermined confidence threshold.

8. A patient monitoring system comprising:

a patient monitoring device configured to acquire physiological data from a patient under observation, the physiological data providing a measurement of at least one physiological parameter; and

a computer in communication with the patient monitoring device to receive physiological data therefrom, the computer programmed to;

receive physiological data from the monitoring device;

select an analysis period that includes at least a portion of the received physiological data, the analysis period having a start date and an end date;

select data sets from the analysis period near the start date and the end date that have a predetermined size without violating normal scatter;

measure a shift confidence interval between the data set near the start date and the data set near the end date using one or more statistical tests; and

combine the shift confidence interval with a fuzzy model to achieve a physiological condition assessment, the fuzzy model describing how the mean associated with the at least one physiological parameter shifts when a predetermined physiological condition is present.

9. The patient monitoring system of claim 8 wherein the computer is further programmed to remove outlying data points from the analysis period physiological data falling outside either an upper or a lower control limit.

10. The patient monitoring system of claim 8 wherein the one or more statistical tests comprise a two-sample t-test.

11. The patient monitoring system of claim 8 wherein the computer is further programmed to provide a plurality of validation cases associated with the system, the validation cases comprising examples of predetermined physiological conditions determined from known shifts in the at least one physiological parameter.

12. The patient monitoring system of claim 11 wherein the computer is further programmed to validate the fuzzy model using the plurality of validation cases.

13. The patient monitoring system of claim 11 wherein the computer is further programmed to provide an evaluation function.

14. The patient monitoring system of claim 13 wherein the computer is further programmed to use the evaluation function to determine how well the fuzzy model differentiates the physiological condition assessment from a plurality of incorrect physiological condition assessments for the plurality of validation cases.

15. The patient monitoring system of claim 14 wherein the computer is further programmed to output a plurality of confidence values for the physiological condition assessment and the plurality of incorrect physiological condition assessments.

16. The patient monitoring system of claim 15 wherein the computer is further programmed to generate an alert when the confidence value for the physiological condition assessment meets a predetermined confidence threshold.

17. The patient monitoring system of claim 15 wherein the computer is further programmed to determine the degree of separation between the physiological condition assessment and the plurality of incorrect physiological condition assessments.

18. The patient monitoring system of claim 17 wherein the computer is further programmed to optimize the degree of separation between the physiological condition assessment and the plurality of incorrect physiological condition assessments by randomly varying the fuzzy model within predetermined guidelines and recalculating the evaluation function.

19. The patient monitoring system of claim 17 wherein the computer is further programmed to select an analysis period based on one of an operator input and an identification of corner points in the received physiological data that provide a largest shift between the data set near the start date and the data set.

20. A computer readable storage medium having a computer program to provide a physiological condition assessment based on trend shifts in physiological data, the computer program comprising a set of instructions that when executed by a computer cause the computer to:

receive physiological data on a plurality of physiological parameters;

determine a trend shift in the plurality of physiological parameters based on a statistical analysis of the physiological data;

input the trend shift into a fuzzy model to identify a patient condition;

validate the fuzzy model using a plurality of validation cases, the validation cases comprising examples of predetermined patient conditions determined from known shifts in the plurality of physiological parameters; and

use an evaluation function to determine how well the fuzzy model differentiates the identified patient condition from a plurality of incorrect patient conditions for the plurality of validation cases.

21. The computer readable storage medium of claim 20 wherein the set of instructions further causes the computer to perform a two-sample t-test on the physiological data to determine a mean shift in the at least one physiological parameter.

22. The computer readable storage medium of claim 20 wherein the set of instructions further causes the computer to output a plurality of confidence values for the identified patient condition and the plurality of incorrect patient conditions.

23. The computer readable storage medium of claim 20 wherein the set of instructions further causes the computer to:

determine the degree of separation between the identified patient condition and the plurality of incorrect patient conditions; and

optimize the degree of separation between the identified patient condition and the plurality of incorrect patient conditions by randomly varying the fuzzy model within predetermined guidelines and recalculating the evaluation function.