Method for Detecting Critical Trends in Multi-Parameter Patient Monitoring and Clinical Data Using Clustering

• US 20080281170A1
• Filed: 10/17/2006
• Published: 11/13/2008
• Est. Priority Date: 11/08/2005
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Eshelman, Larry J., Zhu, Xinxin (Katie)
• Application Number:
  US12/092,986
• Publication Number:
  US 20080281170A1
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• 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 5/7267 involving training the classification device
• 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 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A physiological data analysis component (10) determines a condition of an individual. The physiological data analysis component (10) includes an input component (12) that receives a plurality of different physiological parameters of the individual. A classification component (20) of the physiological data analysis component (10) maps these parameters to a multi-dimensional space having a plurality of regions corresponding to two or more conditions. The classification component (20) determines the condition of the individual based on the region the physiological parameters mapped within. An output component (24) of the physiological data analysis component (10) conveys the condition of the individual to a user of the physiological data analysis component (10).

Claims

1. A physiological data analysis component that determines a condition of an individual, comprising:

an input component that receives a plurality of different physiological parameters of the individual;

a classification component that maps the plurality of physiological parameters to a multi-dimensional space having a plurality of regions corresponding to two or more conditions and determines the condition of the individual based on the region the physiological parameters mapped within; and

an output component that conveys the condition to a user of the component.

2. The physiological data analysis component as set forth in claim 1, wherein the classification component maps two or more sets of physiological parameters obtained at different time intervals and predicts a future condition of the individual based on a trend derived from the mappings.

3. The physiological data analysis component as set forth in claim 2, wherein the classification component performs a time-series analysis to determine the trend.

4. The physiological data analysis component as set forth in claim 2, wherein the classification component generates the trend by connecting two or more mappings through a vector and extrapolating subsequent mapping.

5. The physiological data analysis component as set forth in claim 2, wherein the physiological parameters mapped to the multi-dimensional space include one or more of the following:

temperature;

heart rate;

respiration rate;

systolic blood pressure; and

white blood cell count.

6. The physiological data analysis component as set forth in claim 1, wherein the classification component maps the physiological parameters to the multi-dimensional space through one or more of the following techniques:

clustering, k-means, k-medoids, Expectation Maximization (EM), neural networks, hierarchical methods, probabilistic analysis, statistic analysis, a priori knowledge, classifiers, support vector machines, distance measures, expert systems, Bayesian belief networks, fuzzy logic, pattern recognition, interpolation, extrapolation, data fusion engines, look-up tables and polynomial expansion.

7. The physiological data analysis component as set forth in claim 1, wherein the physiological data includes two or more of heart rate, blood pressure, blood oxygen level, core body temperature, heart electrical activity, white blood count, and hormone level.

8. The physiological data analysis component as set forth in claim 1, wherein the classification component defines one or more regions of stability within the multi-dimensional space by mapping physiological parameters indicative of a stable condition to the multi-dimensional space and labelling these regions as stable.

9. The physiological data analysis component as set forth in claim 1, wherein the classification component defines one or more regions of instability within the multi-dimensional space by mapping physiological parameters indicative of an unstable condition to the multi-dimensional space and labelling these regions based on the unstable condition.

10. The physiological data analysis component as set forth in claim 1 wherein the unstable condition regions are predetermined for patients previously diagnosed with each unstable condition.

11. The physiological data analysis component as set forth in claim 1, further including a messaging component that transmits a notification when the condition of the individual is predicted to change.

12. The physiological data analysis component as set forth in claim 1, further including an output component for conveying at least one of collected data, processed data, and results.

13. A method for determining a condition of an individual, comprising:

receiving a plurality of physiological parameters of the individual; and

determining the condition of the individual by mapping the plurality of physiological parameters to a region in multi-dimensional space that correlates to a particular condition.

14. The method as set forth in claim 13, further comprising:

mapping at least one other set of physiological parameters obtained at a different time interval; and

predicting a future condition of the individual based on a change between the mappings.

15. The method as set forth in claim 14, wherein the change is represented as a vector progressing towards the future condition.

16. The method as set forth in claim 13, further including:

using a multi-dimensional clustering analysis to generate a vector based on the plurality of received physiological parameters.

17. The method as set forth in claim 13, further including:

defining one or more regions within the multi-dimensional space by mapping physiological parameters indicative of one or more conditions to the multi-dimensional space and labelling these regions.

18. The method as set forth in claim 13, further including:

conveying at least one of a message indicative of the condition of the individual, a message indicative of a future condition of the individual, and the physiological parameters.

19. A computer programmed to perform the method of claim 13.

20. A method for determining a present and a future condition of an individual, comprising:

identifying regions of stability and instability within multi-dimensional space;

receiving a set of physiological parameters of the individual;

determining the present condition of the individual by mapping the set of physiological parameters to the multi-dimensional space in which the condition of the individual is based on the region the physiological parameters mapped within;

receiving one or more additional sets of physiological parameters of the individual, each set obtained at a different time;

mapping the one or more additional sets of physiological parameters within the multi-dimensional space;

generating a trend based on the mapped sets of physiological parameters; and

projecting a future condition of the individual based on the trend.