Systems and Methods for Dynamic Charting

1 . A method, comprising:

receiving, by a device, patient data that indicates health related information associated with a patient;

identifying, by the device and by processing the patient data using one or more natural language processing techniques, indicia associated with a health status of the patient;

identifying, by the device, similarities between the indicia and content that is part of a corpus of health related data;

generating, by the device and using an artificial intelligence engine, cognified data based on the similarities;

identifying, by the device, a medical code that correlates to particular content that is similar to the indicia; and

causing, by the device, the cognified data to be displayed in association with the medical code.

2 . The method of claim 1 , wherein identifying the similarities comprises:

comparing the indicia with the content, where the content is stored using a knowledge graph, and

identifying a semantic or semantically-related similarity between a characteristic of the indicia and a corresponding content characteristic; and

wherein generating the cognified data comprises:

generating the cognified data based on the semantic or semantically-related similarity.

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

comparing the indicia with the content, where the content is stored using a knowledge graph, and

identifying, using a logical structure, a structural similarity of the indicia and a known predicate of the logical structure of the knowledge graph; and

wherein generating the cognified data comprises:

generating the cognified data based on the structural similarity.

4 . The method of claim 1 , wherein generating the cognified data comprises:

identifying, using the artificial intelligence engine, a pattern based on a structural similarity between a logical structure of a data structure used to store the indicia and a logical structure of a knowledge graph used to store the content, and

generating the cognified data based on the pattern.

5 . The method of claim 1 , wherein generating the cognified data comprises:

generating the cognified data in real-time or near real-time relative to receiving the health related information;

wherein identifying the medical code comprises:

identifying the medical code in real-time or near real-time relative to receiving the health related information; and

wherein causing the cognified data to be displayed comprises:

causing the cognified data to be displayed in association with the medical code in real-time or near real-time relative to receiving the health related information.

6 . The method of claim 1 , wherein the cognified data provides a summary of the health status for the patient and includes at least one of:

a conclusion,

a recommendation,

a complication,

a risk statement,

a description of a cause of a health complication, or

a description of symptoms of the health complication.

7 . The method of claim 1 , further comprising:

determining, using the cognified data, that particular indicia represents new health information that is not found in the corpus of health related data; and

causing a data structure to be updated with the new health information.

8 . A device, comprising:

one or more processors; and

one or more memories including instructions that, when executed by the one or more processors, cause the one or more processors to:

receive patient data that indicates health related information associated with a patient;

identify, by processing the patient data using one or more natural language processing techniques, indicia associated with a health status of the patient;

identify similarities between the indicia and the content that is part of a corpus of health related data;

generate, using an artificial intelligence engine, cognified data based on the similarities;

identify a medical code that correlates to the content having the content characteristics similar to the characteristics of the indicia; and

cause the cognified data to be displayed in association with medical codes that relate to at least the indicia or the cognified data.

9 . The device of claim 8 , wherein the one or more processors, when identifying the similarities, are to:

compare the indicia with the content, where the content is stored using a knowledge graph, and

identify a semantic or semantically-related similarity between a characteristic of the indicia and a corresponding content characteristic; and

wherein the one or more processors, when generating the cognified data, are to:

generate the cognified data based on the semantic or semantically-related similarity.

10 . The device of claim 8 , wherein the one or more processors, when identifying the similarities, are to:

compare the indicia with the content, where the content is stored using a knowledge graph, and

identify, using a logical structure, a structural similarity of the indicia and a known predicate of the logical structure of the knowledge graph; and

wherein the one or more processors, when generating the cognified data, are to:

generate the cognified data based on the structural similarity.

11 . The device of claim 8 , wherein the one or more processors, when generating the cognified data, are to:

identify, using the artificial intelligence engine, a pattern based on a structural similarity between a logical structure of a data structure used to store the indicia and a logical structure of a knowledge graph used to store the content, and

generate the cognified data based on the pattern.

12 . The device of claim 8 , wherein the one or more processors, when generating the cognified data, are to:

generate the cognified data in real-time or near real-time relative to receiving the health related information.

13 . The method of claim 1 , wherein the cognified data provides a summary of the health status for the patient and includes at least one of:

a conclusion,

a recommendation,

a complication,

a risk statement,

a description of a cause of a health complication, or

a description of symptoms of the health complication.

14 . The device of claim 8 , wherein the one or more processors are further to:

determine, using the cognified data, that particular indicia represents new health information that is not found in the corpus of health related data; and

cause a data structure to be updated with the new health information.

15 . A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to:

receive patient data that indicates health related information associated with a patient;

identify, by processing the patient data using one or more natural language processing techniques, indicia associated with a health status of the patient;

identify similarities between the indicia and content that is part of a corpus of health related data;

generate, using an artificial intelligence engine, cognified data based on the similarities;

identify a medical code that correlates to the content having the content characteristics similar to the characteristics of the indicia; and

cause the cognified data to be displayed in association with the medical code.

16 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, that cause the one or more processors to identify the similarities, cause the one or more processors to:

compare the indicia with the content, where the content is stored using a knowledge graph, and

identify a semantic or semantically-related similarity between a characteristic of the indicia and a corresponding content characteristic; and

wherein the one or more instructions, that cause the one or more processors to generate the cognified data, cause the one or more processors to:

generate the cognified data based on the semantic or semantically-related similarity.

17 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, that cause the one or more processors to identify the similarities, cause the one or more processors to:

compare the indicia with the content, where the content is stored using a knowledge graph, and

identify, using a logical structure, a structural similarity of the indicia and a known predicate of the logical structure of the knowledge graph; and

wherein the one or more instructions, that cause the one or more processors to generate the cognified data, cause the one or more processors to:

generate the cognified data based on the structural similarity.

18 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, that cause the one or more processors to identify the similarities, cause the one or more processors to:

identify, using the artificial intelligence engine, a pattern based on a structural similarity between a logical structure of a data structure used to store the indicia and a logical structure of a knowledge graph used to store the content, and

generate the cognified data based on the pattern.

19 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, that cause the one or more processors to identify the similarities, cause the one or more processors to:

generate the cognified data in real-time or near real-time relative to receiving the health related information.

20 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine, using the cognified data, that particular indicia represents new health information that is not found in the corpus of health related data; and

cause a data structure to be updated with the new health information.