METHOD AND SYSTEM FOR PROPAGATING LABELS TO PATIENT ENCOUNTER DATA

• US 20140244293A1
• Filed: 02/22/2013
• Published: 08/28/2014
• Est. Priority Date: 02/22/2013
• Status: Abandoned Application

• Current Assignee: 3M Innovative Properties Company (3M Company)
• Original Assignee: 3M Innovative Properties Company (3M Company)
• Inventors: Jones, Victor T. III
• Application Number:
  US13/773,835
• Publication Number:
  US 20140244293A1
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 70/60 relating to pathologies

Abstract

Methods and systems for propagating labels, such as “good” or “bad”, to unlabeled patient encounter data. Patient encounters, partially labeled but mostly unlabeled, is represented as nodes in a network, and labels are propagated from labeled nodes to unlabeled nodes based on the similarity of the unlabeled nodes to neighboring nodes. The resulting patient encounter data may be used for, for example, a training data set, or for other purposes.

Claims

1. A computer-implemented method of propagating labels to a set of clinical encounter data, the computer having at least one processor and memory, comprising:

receiving a set of labeled coded encounter data, each coded encounter including encounter-related features associated with a patient'"'"'s encounter with a healthcare organization and a set of codes associated with the encounter-related features, and each coded encounter including a label indicative of a label attribute;

receiving a set of unlabeled patient encounter data; and

,

using at least one of the computer'"'"'s processors, algorithmically propagating labels to the set of unlabeled patient encounter data based on the set of labeled patient encounter data, to produce resulting labeled coded encounter data.

2. The computer-implemented method of claim 1, further comprising:

combining the resulting coded encounter data with the labeled coded encounter data to produce a set of training data.

3. The computer-implemented method of claim 1, wherein for each coded encounter, the label is indicative of agreement between the codes associated with an individual encounter and codes selected by a human coder for the same individual encounter, based upon the human coder'"'"'s review of the patient encounter data.

4. The computer implemented method of claim 3, wherein the label is either indicative of “

good”

or “

bad”

, the label indicative of “

good”

signifying agreement, and the label of “

bad”

signifying the lack of agreement.

5. The computer-implemented method of claim 1, wherein the codes comprise Current Procedure Terminology codes and/or International Classification of Disease codes.

6. The computer-implemented method of claim 2, further comprising:

outputting the set of training data.

7. The computer-implemented method of claim 2, further comprising:

training a confidence assessment module using the training patient encounter data using machine learning techniques.

8. The computer-implemented method of claim 1, wherein algorithmically propagating labels to the set of unlabeled patient encounter data comprises:

algorithmically representing the labeled and unlabeled data as nodes in vector space network, the distance between the nodes a function of the similarity of the encounter-related features;

computing a minimum spanning tree through the nodes to define neighboring nodes; and

,

assigning a label to unlabeled nodes based on the similarity of the unlabeled node to neighboring nodes.

9. The computer-implemented method of claim 8, wherein algorithmically representing the labeled and unlabeled data as nodes in a vector space network comprises having at least two overlapping nodes that have different labels, and wherein computing the minimum spanning tree comprises defining a stopping criteria for the minimum spanning tree, and wherein the stopping criteria accommodate overlapping nodes having different labels.

10. The computer-implemented method of claim 8, wherein the stopping criteria for the minimum spanning tree is based on the homogeneity and size of sub-trees.

11. A system for propagating labels to a set of clinical encounter data, the system implemented on a computer having at least one processor and memory, comprising:

a first storage module containing labeled coded encounter data, each coded encounter including encounter-related features associated with a patient'"'"'s encounter with a healthcare organization and a set of codes associated with the encounter-related features, and each coded encounter including a label indicative of a label attribute;

a second storage module containing unlabeled coded encounter data; and

,

a software-implemented label propagation module operative to;

(a) receive a set of labeled coded encounter data from the first storage module;

(b) receive a set of unlabeled patient encounter data from the second storage module; and

,

(c) algorithmically propagate labels to the set of unlabeled patient encounter data based on the set of labeled patient encounter data, to produce resulting labeled coded encounter data.

12. The system of claim 11, wherein the software-implemented label propagation module is further operative to:

combine the resulting coded encounter data with the labeled coded encounter data to produce a set of training data.

13. The system of claim 11, wherein for each coded encounter, the label is indicative of agreement between the codes associated with an individual encounter and codes selected by a human coder for the same individual encounter, based upon the human coder'"'"'s review of the patient encounter data.

14. The system of claim 13, wherein the label is either indicative of “

good”

or “

bad”

, the label indicative of “

good”

signifying agreement, and the label of “

bad”

signifying the lack of agreement.

15. The system of claim 11, wherein the codes comprise Current Procedure Terminology codes and/or International Classification of Disease codes.

16. The system of claim 12, further comprising:

outputting the set of training data.

17. The system of claim 12, further comprising:

training a confidence assessment module using the training patient encounter data using machine learning techniques.

18. The system of claim 11, wherein to algorithmically propagate labels to the set of unlabeled patient encounter data comprises:

algorithmically representing the labeled and unlabeled data as nodes in vector space network, the distance between the nodes a function of the similarity of the encounter-related features;

computing a minimum spanning tree through the nodes to define neighboring nodes; and

, assigning a label to unlabeled nodes based on the similarity of the unlabeled node to neighboring nodes.

19. The system of claim 18, wherein algorithmically representing the labeled and unlabeled data as nodes in a vector space network comprises having at least two overlapping nodes that have different labels, and wherein computing the minimum spanning tree comprises defining a stopping criteria for the minimum spanning tree, and wherein the stopping criteria accommodate overlapping nodes having different labels.

20. The system of claim 19, wherein the stopping criteria for the minimum spanning tree is based on the homogeneity and size of sub-trees.