ANNOTATING MEDICAL DATA REPRESENTED BY CHARACTERISTIC FUNCTIONS

• US 20120047136A1
• Filed: 08/17/2010
• Published: 02/23/2012
• Est. Priority Date: 08/17/2010
• Status: Active Grant

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: Stergiou, Stergios, Jain, Jawahar
• Granted Patent: US 9,138,143 B2
• Time in Patent Office: Days
• US Class Current: 707/736
• CPC Class Codes: A61B 2560/0295 adapted for recording user messages or annotations
• 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: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

According to certain embodiments, a set of samples of sensor data is accessed. The set of samples records medical measurements taken by one or more medical sensors. A characteristic function is generated from the set of samples. The characteristic function indicates whether a given sample is a member of the set of samples. One or more samples of the set of samples that are associated with a given medical annotation are identified according to the characteristic function.

Claims

1. A method comprising:

accessing a set of samples of sensor data recording a plurality of medical measurements taken by one or more medical sensors;

generating a characteristic function from the set of samples, the characteristic function indicating whether a given sample is a member of the set of samples; and

identifying one or more samples of the set of samples that are associated with a given medical annotation according to the characteristic function.

2. The method of claim 1, the generating the characteristic function comprising:

representing each sample of the set of samples as a minterm to yield a set of minterms; and

generating a characteristic function from the set of minterms.

3. The method of claim 1, the identifying the one or more samples comprising:

receiving a model characteristic function, the model characteristic function representing a set of model samples annotated with one or more medical annotations; and

identifying the one or more samples using the model characteristic function and the characteristic function.

4. The method of claim 1, the identifying the one or more samples comprising:

annotating the characteristic function using a model characteristic function to yield an annotated characteristic function, the model characteristic function representing a set of model samples annotated with one or more medical annotations; and

querying the annotated characteristic function to identify the one or more samples.

5. The method of claim 1, the identifying the one or more samples comprising:

annotating the characteristic function using a model characteristic function to yield an annotated characteristic function, the model characteristic function representing a set of model samples annotated with one or more medical annotations, the annotated characteristic function representing the set of samples annotated with the one or more medical annotations; and

querying the annotated characteristic function to identify the one or more samples.

6. The method of claim 1, the identifying the one or more samples comprising:

determining an annotated characteristic function for the characteristic function, the annotated characteristic function representing the set of samples annotated with one or more medical annotations;

formulating a query function representing the given medical annotation; and

combining the query function and the annotated characteristic function to identify the one or more samples.

7. The method of claim 1, the one or more medical annotations comprising one or more of the following:

a normal annotation and an abnormal annotation.

8. An apparatus comprising:

a memory configured to store a set of samples of sensor data recording a plurality of medical measurements taken by one or more medical sensors; and

one or more processors configured to;

generate a characteristic function from the set of samples, the characteristic function indicating whether a given sample is a member of the set of samples; and

identify one or more samples of the set of samples that are associated with a given medical annotation according to the characteristic function.

9. The apparatus of claim 8, the generating the characteristic function comprising:

representing each sample of the set of samples as a minterm to yield a set of minterms; and

generating a characteristic function from the set of minterms.

10. The apparatus of claim 8, the identifying the one or more samples comprising:

receiving a model characteristic function, the model characteristic function representing a set of model samples annotated with one or more medical annotations; and

identifying the one or more samples using the model characteristic function and the characteristic function.

11. The apparatus of claim 8, the identifying the one or more samples comprising:

annotating the characteristic function using a model characteristic function to yield an annotated characteristic function, the model characteristic function representing a set of model samples annotated with one or more medical annotations; and

querying the annotated characteristic function to identify the one or more samples.

12. The apparatus of claim 8, the identifying the one or more samples comprising:

annotating the characteristic function using a model characteristic function to yield an annotated characteristic function, the model characteristic function representing a set of model samples annotated with one or more medical annotations, the annotated characteristic function representing the set of samples annotated with the one or more medical annotations; and

querying the annotated characteristic function to identify the one or more samples.

13. The apparatus of claim 8, the identifying the one or more samples comprising:

determining an annotated characteristic function for the characteristic function, the annotated characteristic function representing the set of samples annotated with one or more medical annotations;

formulating a query function representing the given medical annotation; and

combining the query function and the annotated characteristic function to identify the one or more samples.

14. The apparatus of claim 8, the one or more medical annotations comprising one or more of the following:

a normal annotation and an abnormal annotation.

15. One or more non-transitory computer-readable media storing code, when executed by one or more processors, configured to:

access a set of samples of sensor data recording a plurality of medical measurements taken by one or more medical sensors;

generate a characteristic function from the set of samples, the characteristic function indicating whether a given sample is a member of the set of samples; and

identify one or more samples of the set of samples that are associated with a given medical annotation according to the characteristic function.

16. The media of claim 15, the generating the characteristic function comprising:

representing each sample of the set of samples as a minterm to yield a set of minterms; and

generating a characteristic function from the set of minterms.

17. The media of claim 15, the identifying the one or more samples comprising:

receiving a model characteristic function, the model characteristic function representing a set of model samples annotated with one or more medical annotations; and

identifying the one or more samples using the model characteristic function and the characteristic function.

18. The media of claim 15, the identifying the one or more samples comprising:

annotating the characteristic function using a model characteristic function to yield an annotated characteristic function, the model characteristic function representing a set of model samples annotated with one or more medical annotations; and

querying the annotated characteristic function to identify the one or more samples.

19. The media of claim 15, the identifying the one or more samples comprising:

annotating the characteristic function using a model characteristic function to yield an annotated characteristic function, the model characteristic function representing a set of model samples annotated with one or more medical annotations, the annotated characteristic function representing the set of samples annotated with the one or more medical annotations; and

querying the annotated characteristic function to identify the one or more samples.

20. The media of claim 15, the identifying the one or more samples comprising:

determining an annotated characteristic function for the characteristic function, the annotated characteristic function representing the set of samples annotated with one or more medical annotations;

formulating a query function representing the given medical annotation; and

combining the query function and the annotated characteristic function to identify the one or more samples.

21. The media of claim 15, the one or more medical annotations comprising one or more of the following:

a normal annotation and an abnormal annotation.