Methods and apparatus for applying user corrections to medical fact extraction

• US 8,756,079 B2
• Filed: 06/08/2012
• Issued: 06/17/2014
• Est. Priority Date: 02/18/2011
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Yegnanarayanan, Girija
• Primary Examiner(s): Sereboff, Neal
• Assistant Examiner(s): KANAAN, MAROUN P
• Application Number:
  US13/492,276
• Publication Number:
  US 20120245961A1
• Time in Patent Office: 739 Days
• Field of Search: 705/2, 705/3
• US Class Current: 705/3
• 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 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof

Abstract

A method for applying a user correction to medical fact extraction may include 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. A correction to the first set of medical facts may be received from a user documenting the patient encounter. 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. An apparatus for applying a user correction to medical fact extraction may include at least one processor and at least one computer-readable storage medium storing processor-executable instructions that, when executed by the at least one processor, perform the above-described method. At least one computer-readable storage medium may be encoded with computer-executable instructions that perform the above-described method.

Claims

1. A method comprising:

accessing a free-form text documenting a patient encounter;

analyzing the free-form text, using at least one processor, to extract a first set of one or more medical facts from a first portion of the free-form text, the analyzing comprising

processing the free-form text to identify a set of one or more features of the first portion of the free-form text,

correlating the set of features to one or more abstract semantic concepts, and

generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more medical facts extracted from the first portion of the free-form text;

receiving, from a user documenting the patient encounter, a correction to the first set of medical facts extracted from the first portion of the free-form text; and

applying the user'"'"'s correction in extracting a second set of one or more medical facts from a second portion of the free-form text.

2. The method of claim 1, wherein the user'"'"'s correction comprises removing a fact from the first set of facts, and wherein applying the user'"'"'s correction in extracting the second set of facts comprises suppressing extraction of one or more facts similar to the removed fact from the second portion of the free-form text.

3. The method of claim 1, wherein the user'"'"'s correction comprises adding a fact to the first set of facts, and wherein applying the user'"'"'s correction in extracting the second set of facts comprises extracting one or more facts similar to the identified fact from the second portion of the free-form text.

4. Apparatus comprising:

at least one processor; and

at least one computer-readable storage medium storing processor-executable instructions that, when executed by the at least one processor, perform a method comprising;

accessing a free-form text documenting a patient encounter;

analyzing the free-form text to extract a first set of one or more medical facts from a first portion of the free-form text, the analyzing comprising

processing the free-form text to identify a set of one or more features of the first portion of the free-form text,

correlating the set of features to one or more abstract semantic concepts, and

generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more medical facts extracted from the first portion of the free-form text;

receiving, from a user documenting the patient encounter, a correction to the first set of medical facts extracted from the first portion of the free-form text; and

applying the user'"'"'s correction in extracting a second set of one or more medical facts from a second portion of the free-form text.

5. The apparatus of claim 4, wherein the user'"'"'s correction comprises removing a fact from the first set of facts, and wherein applying the user'"'"'s correction in extracting the second set of facts comprises suppressing extraction of one or more facts similar to the removed fact from the second portion of the free-form text.

6. The apparatus of claim 4, wherein the user'"'"'s correction comprises adding a fact to the first set of facts, and wherein applying the user'"'"'s correction in extracting the second set of facts comprises extracting one or more facts similar to the identified fact from the second portion of the free-form text, narrative.

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

accessing a free-form text documenting a patient encounter;

analyzing the free-form text to extract a first set of one or more medical facts from a first portion of the free-form text, the analyzing comprising

processing the free-form text to identify a set of one or more features of the first portion of the free-form text,

correlating the set of features to one or more abstract semantic concepts, and

generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more medical facts extracted from the first portion of the free-form text;

receiving, from a user documenting the patient encounter, a correction to the first set of medical facts extracted from the first portion of the free-form text; and

applying the user'"'"'s correction in extracting a second set of one or more medical facts from a second portion of the free-form text.

8. The at least one non-transitory computer-readable storage medium of claim 7, wherein the user'"'"'s correction comprises removing a fact from the first set of facts, and wherein applying the user'"'"'s correction in extracting the second set of facts comprises suppressing extraction of one or more facts similar to the removed fact from the second portion of the free-form text.

9. The at least one non-transitory computer-readable storage medium of claim 7, wherein the user'"'"'s correction comprises adding a fact to the first set of facts, and wherein applying the user'"'"'s correction in extracting the second set of facts comprises extracting one or more facts similar to the identified fact from the second portion of the free-form text.