Active Electronic Medical Record Based Support System Using Learning Machines

• US 20100082506A1
• Filed: 09/30/2008
• Published: 04/01/2010
• Est. Priority Date: 09/30/2008
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Metz, Stephen W., Choubcy, Suresh K., Barber, Michael J., Deaven, David M., Sirohey, Saad Ahmed, Avinash, Gopal B.
• Application Number:
  US12/242,319
• Publication Number:
  US 20100082506A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/56 Details of data transmission or power supply, e.g. use of slip rings
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A data processing technique is provided. In one embodiment, a computer-implemented method includes receiving image data from an imaging system and organizing the image data into multiple objects of interest. The method may also include identifying source-invariant features of the multiple objects of interest and classifying the multiple objects of interest via a learning algorithm into categories based, at least in part, on the identified source-invariant features. Further, the method may include outputting a report based at least in part on data derived from the classification of one or more of the multiple objects of interest. Additional methods, systems, and devices are also disclosed.

Claims

1. A system comprising:

a memory device having a plurality of routines stored therein;

a processor configured to execute the plurality of routines stored in the memory device, the plurality of routines comprising;

a routine configured to effect, when executed, receiving of input data from a data source;

a routine configured to effect, when executed, organizing of the input data;

a routine configured to effect, when executed, identifying of one or more features of an object of interest from the input data, wherein the identifying of the feature includes identifying one or more source-invariant characteristics of the object of interest;

a routine configured to effect, when executed, classifying of the object of interest via a learning algorithm, wherein the classifying of the object of interest is based, at least in part, on the one or more identified source-invariant characteristics; and

a routine configured to effect, when executed, outputting of results of the classification of the object of interest.

2. The system of claim 1, wherein the input data includes image data and non-image data.

3. The system of claim 2, wherein the classifying of the object of interest is based, at least in part, on both the image data and the non-image data.

4. The system of claim 1, wherein the data source includes an imaging system configured to acquire image data pertaining to a patient.

5. The system of claim 1, wherein the data source includes a database of patient information.

6. The system of claim 1, wherein the one or more source-invariant characteristics of the object of interest include geometric characteristics, textural characteristics, or density of the object of interest.

7. The system of claim 1, wherein the plurality of routines comprise a routine configured to effect, when executed, organizing of the results of the classification of the object of interest.

8. The system of claim 7, wherein the organizing of the results includes indexing the results, processing the indexed results, and generating at least one output.

9. The system of claim 8, wherein the outputting of the results includes outputting at least one of a graphical output or an alarm output.

10. The system of claim 1, wherein the outputting of the results includes one or more of:

providing an indication of the results to a user via a computing device;

transmitting the results to an automated tool for additional processing;

or

storing the results for future output or processing.

11. A computer-implemented method comprising:

receiving image data from an imaging system;

organizing the image data into multiple objects of interest;

identifying source-invariant features of the multiple objects of interest;

classifying the multiple objects of interest via a learning algorithm into categories based, at least in part, on the identified source-invariant features; and

outputting a report based at least in part on data derived from the classification of one or more of the multiple objects of interest.

12. The computer-implemented method of claim 11, wherein classifying the multiple objects of interest via the learning algorithm is performed independent of source-varying features.

13. The computer-implemented method of claim 11, comprising:

receiving non-image data; and

classifying the multiple objects of interest via the learning algorithm into categories based, at least in part, on the non-image data as well as the identified source-invariant features.

14. The computer-implemented method of claim 11, wherein the learning algorithm includes a support vector machine.

15. A method comprising:

providing an initial problem definition to a medical institution, the initial problem definition including a process for predicting a diagnostic outcome regarding objects detected in medical image data through analysis of at least the medical image data;

receiving diagnostic data from the medical institution regarding a detected object in the medical image data;

comparing the diagnostic data with the predicted diagnostic outcome regarding the detected object;

revising the initial problem definition based, at least in part, on the comparison;

training a learning machine based, at least in part, on the diagnostic data received from the medical institution;

operating the learning machine to analyze a medical image and to generate a predicted diagnostic outcome with respect to an object detected in the medical image; and

outputting a report indicative of a result of the analysis of the medical image by the learning machine.

16. The method of claim 15, wherein training the learning machine comprises training a classification algorithm, further comprising distributing the classification algorithm for installation on an additional machine, such that the additional machine is configured to analyze medical images and generate predicted diagnostic outcomes via the classification algorithm.

17. The method of claim 16, wherein distributing the classification algorithm comprises at least one of:

transmitting the classification algorithm over a network;

or

providing a computer-readable media having the classification algorithm encoded thereon.

18. The method of claim 17, wherein distributing the classification algorithm comprises distributing a computer program including the classification algorithm.

19. A manufacture comprising:

a computer-readable medium having executable instructions stored thereon, the executable instructions comprising;

instructions adapted to receive image data from an imaging system;

instructions adapted to organize the image data into multiple objects of interest;

instructions adapted to identify source-invariant features of the multiple objects of interest;

instructions adapted to classify the multiple objects of interest via a learning algorithm into categories based, at least in part, on the identified source-invariant features; and

instructions adapted to output a report based at least in part on data derived from the classification of one or more of the multiple objects of interest.

20. The manufacture of claim 19, wherein the computer-readable medium comprises a plurality of computer-readable media at least collectively having the executable instructions stored thereon.