METHODS AND APPARATUS FOR APPLYING USER CORRECTIONS TO MEDICAL FACT EXTRACTION

• US 20130035961A1
• Filed: 10/12/2012
• Published: 02/07/2013
• Est. Priority Date: 02/18/2011
• Status: Active Grant

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Yegnanarayanan, Girija
• Granted Patent: US 8,694,335 B2
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 2015/228 of application context
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Techniques for applying user corrections to medical fact extraction may include extracting a first set of one or more medical facts from a first portion of text documenting a patient encounter. A correction to the first set of medical facts may be received from a user. The correction may identify a fact that should be associated with the first portion of the text. A second set of one or more medical facts may be extracted from a second portion of the text based at least in part on the user'"'"'s correction to the first set of medical facts. Extracting the second set of facts may include extracting one or more facts similar to the identified fact from the second portion of the text.

Claims

1. A method comprising:

extracting, using at least one processor, a first set of one or more medical facts from a first portion of a text documenting a patient encounter;

receiving, from a user, a correction to the first set of medical facts, wherein the correction identifies a fact that should be associated with the first portion of the text; and

extracting a second set of one or more medical facts from a second portion of the text based at least in part on the user'"'"'s correction to the first set of medical facts, wherein extracting the second set of facts comprises extracting one or more facts similar to the identified fact from the second portion of the text.

2. The method of claim 1, wherein the identified fact corresponds to a particular fact type, and wherein extracting the second set of facts comprises labeling one or more tokens in the second portion of the text as corresponding to the particular fact type.

3. The method of claim 1, wherein at least one of the one or more facts similar to the identified fact has a same normalized form as the identified fact.

4. The method of claim 1, wherein extracting the second set of facts comprises establishing one or more rules correlating features of the first portion of the text with the identified fact, and applying at least one of the one or more rules to another portion of the text that shares at least some of the features of the first portion.

5. The method of claim 1, wherein extracting the second set of facts comprises establishing a rule assigning one or more features correlated with the identified fact to a token in the first portion of the text, and applying the rule to another portion of the text that also includes the token.

6. The method of claim 1, further comprising:

creating updated training data by adding the first portion of the text, labeled with the identified fact, to original training data for a statistical model used to extract the medical facts; and

re-training the model using the updated training data.

7. The method of claim 6, wherein the re-training comprises weighting the first portion of the text more heavily than the original training data in the updated training data used to re-train the model.

8. Apparatus comprising:

at least one processor; and

at least one memory storing processor-executable instructions that, when executed by the at least one processor, perform a method comprising;

extracting a first set of one or more medical facts from a first portion of a text documenting a patient encounter;

receiving, from a user, a correction to the first set of medical facts, wherein the correction identifies a fact that should be associated with the first portion of the text; and

extracting a second set of one or more medical facts from a second portion of the text based at least in part on the user'"'"'s correction to the first set of medical facts, wherein extracting the second set of facts comprises extracting one or more facts similar to the identified fact from the second portion of the text.

9. The apparatus of claim 8, wherein the identified fact corresponds to a particular fact type, and wherein extracting the second set of facts comprises labeling one or more tokens in the second portion of the text as corresponding to the particular fact type.

10. The apparatus of claim 8, wherein at least one of the one or more facts similar to the identified fact has a same normalized form as the identified fact.

11. The apparatus of claim 8, wherein extracting the second set of facts comprises establishing one or more rules correlating features of the first portion of the text with the identified fact, and applying at least one of the one or more rules to another portion of the text that shares at least some of the features of the first portion.

12. The apparatus of claim 8, wherein extracting the second set of facts comprises establishing a rule assigning one or more features correlated with the identified fact to a token in the first portion of the text, and applying the rule to another portion of the text that also includes the token.

13. The apparatus of claim 8, wherein the method further comprises:

creating updated training data by adding the first portion of the text, labeled with the identified fact, to original training data for a statistical model used to extract the medical facts; and

re-training the model using the updated training data.

14. At least one computer-readable storage medium encoded with computer-executable instructions that, when executed, perform a method comprising:

extracting a first set of one or more medical facts from a first portion of a text documenting a patient encounter;

receiving, from a user, a correction to the first set of medical facts, wherein the correction identifies a fact that should be associated with the first portion of the text; and

extracting a second set of one or more medical facts from a second portion of the text based at least in part on the user'"'"'s correction to the first set of medical facts, wherein extracting the second set of facts comprises extracting one or more facts similar to the identified fact from the second portion of the text.

15. The at least one computer-readable storage medium of claim 14, wherein the identified fact corresponds to a particular fact type, and wherein extracting the second set of facts comprises labeling one or more tokens in the second portion of the text as corresponding to the particular fact type.

16. The at least one computer-readable storage medium of claim 14, wherein at least one of the one or more facts similar to the identified fact has a same normalized form as the identified fact.

17. The at least one computer-readable storage medium of claim 14, wherein extracting the second set of facts comprises establishing one or more rules correlating features of the first portion of the text with the identified fact, and applying at least one of the one or more rules to another portion of the text that shares at least some of the features of the first portion.

18. The at least one computer-readable storage medium of claim 14, wherein extracting the second set of facts comprises establishing a rule assigning one or more features correlated with the identified fact to a token in the first portion of the text, and applying the rule to another portion of the text that also includes the token.

19. The at least one computer-readable storage medium of claim 14, wherein the method further comprises:

creating updated training data by adding the first portion of the text, labeled with the identified fact, to original training data for a statistical model used to extract the medical facts; and

re-training the model using the updated training data.

20. The at least one computer-readable storage medium of claim 19, wherein the re-training comprises weighting the first portion of the text more heavily than the original training data in the updated training data used to re-train the model.