Granted Patent B2

US 12,216,799 · App. 18/381,873 · Granted Feb 4, 2025

Inventors: Sankar Ardhanari (Chapel Hill, NC); Karthik Murugadoss (Cambridge, MA); Murali Aravamudan (Andover, MA); Ajit Rajasekharan (West Windsor, NJ)

Assignee: nference, Inc.

G06F21/6254G06N20/00G16H10/60

Abstract

Claims (52)

1. A de-identification method comprising:

receiving a plurality of data sets, wherein the plurality of data sets comprises:

a first data set, wherein the first data set comprises a labeled data set for one or more entity types; and

a second data set, wherein the training data set comprises an unlabeled data set for the one or more entity types;

determining one machine-learning model from a plurality of machine-learning models for each of one or more entity types;

fine-tuning the determined machine-learning model for each of the one or more entity types, wherein fine-tuning the determined machine-learning model comprises:

creating a plurality of training data sets, wherein the plurality of training data sets comprises:

a first training data set, wherein the first training data set comprises the first data set; and

a second training data set, wherein the second training data set comprises the second data set;

training the determined machine-learning model using the first training data set;

validating the trained machine-learning model, wherein validating the trained machine learning model further comprises:

generating a recall score for each entity type of the one or more entity types;

comparing the recall score to a threshold for the recall score for each entity type of the one or more entity types; and

updating the trained machine-learning model using the second training data set as a function of the validation; and

obfuscating the second data set using the fine-tuned machine-learning model.

2. The de-identification method of claim 1 , wherein obfuscating the second data set further comprises replacing two or more entities that refer to a common subject with a common surrogate.

3. The de-identification method of claim 2 , further comprising:

selecting the common surrogate based on one or more attributes of the two or more entities.

4. The de-identification method of claim 2 , further comprising:

selecting the common surrogate based on a gender associated with the two or more entities.

5. The de-identification method of claim 2 , further comprising:

selecting the common surrogate based on an ethnicity associated with the two or more entities.

6. The de-identification method of claim 1 , wherein validating the trained machine learning model further comprises:

generating a precision score for each entity type of the one or more entity types; and

comparing the precision score to a threshold for the precision score for each entity type of the one or more entity types.

7. The de-identification method of claim 1 , wherein validating the trained machine learning model further comprises:

generating a F-score for each entity type of the one or more entity types; and

comparing the F-score to a threshold for the F-score for each entity type of the one or more entity types.

8. The de-identification method of claim 1 , further comprising:

updating the trained machine-learning model as a function of a comparison of an average of a F-score, a precision score and the recall score to a threshold success percentage.

9. The de-identification method of claim 1 , wherein:

the one or more entity types comprises two or more personal names; and

obfuscating the second data set further comprises replacing each of the two or more personal names with a different surrogate.

10. The de-identification method of claim 1 , wherein obfuscating the second data set further comprises replacing two or more entities that refer to a common person with surrogates that match a gender associated with the common person.

11. The de-identification method of claim 1 , wherein obfuscating the second data set further comprises replacing two or more entity types that refer to a common person with surrogates that match an ethnicity associated with the common person.

12. The de-identification method of claim 1 , wherein:

obfuscating the second data set further comprises replacing two or more entities that represent dates with surrogate dates, wherein:

the surrogate dates are based on the two or more entity types altered by a random value.

13. The de-identification method of claim 12 , wherein the surrogate dates associated with a common patient are altered by the same random value.

14. The de-identification method of claim 1 , wherein obfuscating the second data set further comprises scrambling two or more entities that represent numeric identifiers with random values to scramble the numeric identifiers.

15. The de-identification method of claim 1 , wherein the one or more entity types comprises at least a portion of an electronic health record.

16. The de-identification method of claim 1 , further comprising:

receiving a text sequence; and

tagging one or more entities in the text sequence.

17. The de-identification method of claim 16 , further comprising:

aggregating the tagged entities from the text sequence; and

passing the aggregated tagged entities through one or more dreg filters, wherein each of the one or more dreg filters is configured to filter a corresponding entity type based on a rule-based template.

18. The de-identification method of claim 17 , further comprising, creating the rule-based template, wherein creating the rule-based template comprises:

mapping each of one or more portions of the text sequence to a corresponding syntax template;

identifying a candidate syntax template based on a second machine learning model that infers one or more candidate syntax templates based on the one or more portions of the text sequence; and

creating the rule-based template from the candidate syntax template by replacing each of the one or more tagged entities in the portion of the text sequence corresponding to the candidate template with a corresponding syntax token.

19. The de-identification method of claim 17 , wherein each of the one or more dreg filters is further configured to filter the corresponding entity type based on a pattern-based filter.

Assignments (1)

Continuity (11)

References Cited (94)