IP Library › Patent Application 18562191
Patent Application
App. No. 18/562,191

METHOD AND SYSTEM FOR AGGREGATING DATA

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Patent No.
US None
App. No.
18/562,191
Abstract

This invention relates generally to the medical technology field, and more specifically to a new and useful method for aggregating data.

Claims (137)

1 . A method for aggregating data comprising:

receiving a data source relating to a patient, the data source containing a data set;

matching data in the data set to at least one of a set of predetermined attributes and a set of predetermined properties to produce a set of extracted attributes and a set of observed properties for the patient, wherein each extracted attribute is a predetermined attribute that matches first data in the data set, and each observed property is a predetermined property that matches second data in the data set;

for at least one attribute in the set of extracted attributes, identifying a set of ontologies that maps to the at least one attribute;

determining a set of respective inferred properties for the at least one attribute based on the set of ontologies;

merging, according to a set of merging rules associated with the set of ontologies, the set of extracted attributes, the set of observed properties, and the set of respective inferred properties into a set of database records corresponding to a profile of the patient; and

linking database records corresponding to a plurality of extracted attributes, observed properties, and inferred properties within the profile of the patient based on the set of ontologies.

2 . The method of claim 1 , further comprising:

verifying, based on the set of observed properties and the set of respective inferred properties, the set of extracted attributes.

3 . The method of claim 1 , wherein the merging comprises:

determining that multiple attributes in the set of extracted attributes correspond to different versions of a same information; and

consolidating the multiple attributes to a single attribute within the set of extracted attributes.

4 . The method of claim 3 , wherein a first attribute in the multiple attributes has a first observed property, a second attribute in the multiple attributes has a second observed property, and the single attribute has both the first observed property and the second observed property.

5 . The method of claim 4 , wherein the first observed property is a start date and the second observed property is an end date.

6 . The method of claim 3 , further comprising repeating the determining and consolidating for each group of multiple attributes in the set of extracted attributes to generate a set of single attributes within the set of extracted attributes.

7 . The method of claim 6 , wherein each single attribute within the set of extracted attributes is associated with at least one date, the merging further comprising:

displaying, in a user interface generated from the profile of the patient, each single attribute within the set of extracted attributes along a timeline based on its respective at least one date.

8 . The method of claim 1 , wherein each extracted attribute in the set of extracted attributes is related to a respective subset of observed properties in the set of observed properties for the patient.

9 . The method of claim 8 , wherein the merging comprises displaying, in a user interface generated from the profile of the patient, each extracted attribute together with its respective subset of observed properties.

10 . The method of claim 1 , wherein each extracted attribute in the set of extracted attributes is related to a respective subset of inferred properties in the set of inferred properties for the patient.

11 . The method of claim 1 , wherein the matching comprises:

parsing the data in the data set into a set of medical terms using natural language processing; and

matching each medical term in the set of medical terms to the at least one of the set of predetermined attributes and the set of predetermined properties.

12 . The method of claim 1 , wherein the matching comprises:

determining word embeddings from the data in the data set; and

comparing the word embeddings against the at least one of the set of predetermined attributes and the set of predetermined properties to produce the set of extracted attributes and the set of observed properties for the patient.

13 . The method of claim 12 , wherein the comparing comprises fuzzy matching.

14 . The method of claim 1 , wherein the matching comprises:

extracting statements from the data in the data set using a model trained to extract statements associated with a given attribute type; and

adding the extracted statements to at least one of the set of extracted attributes and the set of observed properties for the patient.

15 . The method of claim 1 , wherein the set of predetermined attributes comprises a set of predetermined attribute types, and wherein the matching comprises:

extracting statements from the data in the data set using a learning model trained to identify medical terms;

classifying each extracted statement as one of the predetermined set of attribute types; and

adding the classified statement as an attribute in the set of extracted attributes for the patient.

16 . The method of claim 1 , wherein the set of extracted attributes is an existing set of extracted attributes and the set of observed properties is an existing set of observed properties, the method further comprising:

receiving a new data source relating to the patient, the new data source containing a new data set;

matching the new data set to the set of predetermined attributes to produce a new set of extracted attributes and a new set of observed properties for the patient;

determining that a new attribute in the new set of extracted attributes is the same as an existing attribute in the existing set of extracted attributes; and

merging any new observed properties corresponding to the new attribute into the set of database records corresponding to the profile of the patient for the existing attribute.

17 . The method of claim 1 , wherein identifying a set of ontologies comprises:

determining a standardized code corresponding to the at least one attribute;

sending the standardized code to an ontology lookup service; and

returning all ontologies in a datastore associated with the ontology lookup service that are tagged as related to the standardized code.

18 . The method of claim 1 , further comprising:

repeating the receiving, matching, identifying, querying, and merging for at least one other patient.

19 . A system for aggregating data comprising:

a processor; and

a memory having instructions stored thereon that, when executed, cause the processor to perform operations comprising:

receiving a data source relating to a patient, the data source containing a data set;

matching data in the data set to at least one of a set of predetermined attributes and a set of predetermined properties to produce a set of extracted attributes and a set of observed properties for the patient, wherein each extracted attribute is a predetermined attribute that matches first data in the data set, and each observed property is a predetermined property that matches second data in the data set;

for at least one attribute in the set of extracted attributes, identifying a set of ontologies that maps to the at least one attribute;

determining a set of respective inferred properties for the at least one attribute based on the set of ontologies;

merging, according to a set of merging rules associated with the set of ontologies, the set of extracted attributes, the set of observed properties, and the set of respective inferred properties into a set of database records corresponding to a profile of the patient; and

linking database records corresponding to a plurality of extracted attributes, observed properties, and inferred properties within the profile of the patient based on the set of ontologies.

20 . The system of claim 19 , the operations further comprising:

verifying, based on the set of observed properties and the set of respective inferred properties, the set of extracted attributes.

21 . The system of claim 19 , wherein the merging comprises:

determining that multiple attributes in the set of extracted attributes correspond to different versions of a same information; and

consolidating the multiple attributes to a single attribute within the set of extracted attributes.

22 . The system of claim 21 , wherein a first attribute in the multiple attributes has a first observed property, a second attribute in the multiple attributes has a second observed property, and the single attribute has both the first observed property and the second observed property.

23 . The system of claim 22 , wherein the first observed property is a start date and the second observed property is an end date.

24 . The system of claim 21 , the operations further comprising repeating the determining and consolidating for each group of multiple attributes in the set of extracted attributes to generate a set of single attributes within the set of extracted attributes.

25 . The system of claim 24 , wherein each single attribute within the set of extracted attributes is associated with at least one date, the merging further comprising:

displaying, in a user interface generated from the profile of the patient, each single attribute within the set of extracted attributes along a timeline based on its respective at least one date.

26 . The system of claim 19 , wherein each extracted attribute in the set of extracted attributes is related to a respective subset of observed properties in the set of observed properties for the patient.

27 . The system of claim 26 , wherein the merging comprises displaying, in a user interface generated from the profile of the patient, each extracted attribute together with its respective subset of observed properties.

28 . The system of claim 19 , wherein each extracted attribute in the set of extracted attributes is related to a respective subset of inferred properties in the set of inferred properties for the patient.

29 . The system of claim 19 , wherein the matching comprises:

parsing the data in the data set into a set of medical terms using natural language processing; and

matching each medical term in the set of medical terms to the at least one of the set of predetermined attributes and the set of predetermined properties.

30 . The system of claim 19 , wherein the matching comprises:

determining word embeddings from the data in the data set; and

comparing the word embeddings against the at least one of the set of predetermined attributes and the set of predetermined properties to produce the set of extracted attributes and the set of observed properties for the patient.

31 . The system of claim 30 , wherein the comparing comprises fuzzy matching.

32 . The system of claim 19 , wherein the matching comprises:

extracting statements from the data in the data set using a model trained to extract statements associated with a given attribute type; and

adding the extracted statements to at least one of the set of extracted attributes and the set of observed properties for the patient.

33 . The system of claim 19 , wherein the set of predetermined attributes comprises a set of predetermined attribute types, and wherein the matching comprises:

extracting statements from the data in the data set using a learning module trained to identify medical terms;

classifying each extracted statement as one of the predetermined set of attribute types; and

adding the classified statement as an attribute in the set of extracted attributes for the patient.

34 . The system of claim 19 , wherein the set of extracted attributes is an existing set of extracted attributes and the set of observed properties is an existing set of observed properties, the operations further comprising:

receiving a new data source relating to the patient, the new data source containing a new data set;

matching the new data set to the set of predetermined attributes to produce a new set of extracted attributes and a new set of observed properties for the patient;

determining that a new attribute in the new set of extracted attributes is the same as an existing attribute in the existing set of extracted attributes; and

merging any new observed properties corresponding to the new attribute into the set of database records corresponding to the profile of the patient for the existing attribute.

35 . The system of claim 19 , wherein identifying a set of ontologies comprises:

determining a standardized code corresponding to the at least one attribute;

sending the standardized code to an ontology lookup service; and

returning all ontologies in a datastore associated with the ontology lookup service that are tagged as related to the standardized code.

36 . The system of claim 19 , the operations further comprising:

repeating the receiving, matching, identifying, querying, and merging for at least one other patient.

37 . A non-transitory computer readable storage medium having instructions stored thereon that, when executed, cause a computer system to perform operations comprising:

receiving a data source relating to a patient, the data source containing a data set;

matching data in the data set to at least one of a set of predetermined attributes and a set of predetermined properties to produce a set of extracted attributes and a set of observed properties for the patient, wherein each extracted attribute is a predetermined attribute that matches first data in the data set, and each observed property is a predetermined property that matches second data in the data set;

for at least one attribute in the set of extracted attributes, identifying a set of ontologies that maps to the at least one attribute;

determining a set of respective inferred properties for the at least one attribute based on the set of ontologies;

merging, according to a set of merging rules associated with the set of ontologies, the set of extracted attributes, the set of observed properties, and the set of respective inferred properties into a set of database records corresponding to a profile of the patient; and

linking database records corresponding to a plurality of extracted attributes, observed properties, and inferred properties within the profile of the patient based on the set of ontologies.

38 . The non-transitory computer readable storage medium of claim 37 , the operations further comprising:

verifying, based on the set of observed properties and the set of respective inferred properties, the set of extracted attributes.

39 . The non-transitory computer readable storage medium of claim 37 , wherein the merging comprises:

determining that multiple attributes in the set of extracted attributes correspond to different versions of a same information; and

consolidating the multiple attributes to a single attribute within the set of extracted attributes.

40 . The non-transitory computer readable storage medium of claim 39 , wherein a first attribute in the multiple attributes has a first observed property, a second attribute in the multiple attributes has a second observed property, and the single attribute has both the first observed property and the second observed property.

41 . The non-transitory computer readable storage medium of claim 40 , wherein the first observed property is a start date and the second observed property is an end date.

42 . The non-transitory computer readable storage medium of claim 39 , the operations further comprising repeating the determining and consolidating for each group of multiple attributes in the set of extracted attributes to generate a set of single attributes within the set of extracted attributes.

43 . The non-transitory computer readable storage medium of claim 42 , wherein each single attribute within the set of extracted attributes is associated with at least one date, the merging further comprising:

displaying, in a user interface generated from database profile of the patient, each single attribute within the set of extracted attributes along a timeline based on its respective at least one date.

44 . The non-transitory computer readable storage medium of claim 37 , wherein each extracted attribute in the set of extracted attributes is related to a respective subset of observed properties in the set of observed properties for the patient.

45 . The non-transitory computer readable storage medium of claim 44 , wherein the merging comprises displaying, in a user interface generated from the profile of the patient, each extracted attribute together with its respective subset of observed properties.

46 . The non-transitory computer readable storage medium of claim 37 , wherein each extracted attribute in the set of extracted attributes is related to a respective subset of inferred properties in the set of inferred properties for the patient.

47 . The non-transitory computer readable storage medium of claim 37 , wherein the matching comprises:

parsing the data in the data set into a set of medical terms using natural language processing; and

matching each medical term in the set of medical terms to the at least one of the set of predetermined attributes and the set of predetermined properties.

48 . The non-transitory computer readable storage medium of claim 37 , wherein the matching comprises:

determining word embeddings from the data in the data set; and

comparing the word embeddings against the at least one of the set of predetermined attributes and the set of predetermined properties to produce the set of extracted attributes and the set of observed properties for the patient.

49 . The non-transitory computer readable storage medium of claim 48 , wherein the comparing comprises fuzzy matching.

50 . The non-transitory computer readable storage medium of claim 37 , wherein the matching comprises:

extracting statements from the data in the data set using a model trained to extract statements associated with a given attribute type; and

adding the extracted statements to at least one of the set of extracted attributes and the set of observed properties for the patient.

51 . The non-transitory computer readable storage medium of claim 37 , wherein the set of predetermined attributes comprises a set of predetermined attribute types, and wherein the matching comprises:

extracting statements from the data in the data set using a learning model trained to identify medical terms;

classifying each extracted statement as one of the predetermined set of attribute types; and

adding the classified statement as an attribute in the set of extracted attributes for the patient.

52 . The non-transitory computer readable storage medium of claim 37 , wherein the set of extracted attributes is an existing set of extracted attributes and the set of observed properties is an existing set of observed properties, the operations further comprising:

receiving a new data source relating to the patient, the new data source containing a new data set;

matching the new data set to the set of predetermined attributes to produce a new set of extracted attributes and a new set of observed properties for the patient;

determining that a new attribute in the new set of extracted attributes is the same as an existing attribute in the existing set of extracted attributes; and

merging any new observed properties corresponding to the new attribute into the set of database records corresponding to the profile of the patient for the existing attribute.

53 . The non-transitory computer readable storage medium of claim 37 , wherein identifying a set of ontologies comprises:

determining a standardized code corresponding to the at least one attribute;

sending the standardized code to an ontology lookup service; and

returning all ontologies in a datastore associated with the ontology lookup service that are tagged as related to the standardized code.

54 . The non-transitory computer readable storage medium of claim 37 , the operations further comprising:

repeating the receiving, matching, identifying, querying, and merging for at least one other patient.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 10, 2024
From: INVITAE CORPORATION; CIITIZEN, LLC
To: CITIZEN HEALTH, INC.
Reel/Frame 066087/0060 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 14, 2023
From: RAI, PEEYUSH; CARLSEN, BRIAN; NILAKH, VIRAJ; WEST-ODELL, LISANDRA
To: CIITIZEN CORPORATION
Reel/Frame 065870/0088 →
MERGER AND CHANGE OF NAME Recorded Dec 14, 2023
From: CIITIZEN CORPORATION; CAYMAN MERGER SUB B LLC
To: CIITIZEN, LLC
Reel/Frame 065870/0208 →
RELEASE OF SECURITY INTEREST AT R/F 63787/0148 Recorded Dec 14, 2023
From: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION
To: CIITIZEN, LLC
Reel/Frame 066017/0791 →