System and method for prioritization and presentation of heterogeneous medical data

1. A method, comprising:

retrieving by a controller engine of a processor medical data of a patient, the medical data including data from multiple data sources;

extracting by a concept extraction engine of the processor ontology and medical concepts from the medical data of the patient including free-text documents by processing natural language;

phenotyping, using a machine learning model, by a phenotyping engine of the processor the medical data of the patient and the ontology and medical concepts to generate a reasoning trail and reasoning outcome including one or more medical codes and concepts and converting the one or more medical codes and concepts to a predetermined medical ontology, the reasoning trail including a basis for which the reasoning outcome is determined;

mapping by a concept mapping engine of the processor medical codes and concepts onto a medical vocabulary or ontology from one ontology to another and proceeding to the following assigning step; or when all of the one or more concepts and medical codes are in the same ontology proceeding directly to the following assigning step;

assigning by a relevance determination engine of the processor a relevance score to each of the one or more medical codes and concepts;

ranking by the controller engine the medical codes and concepts based on the relevance score of each of the one or more medical codes and concepts;

color coding at least one of the ranked medical codes or concepts; and

displaying the color coded medical codes and concepts in rank order.

2. The method of claim 1 , wherein a subset of the relevance scores of the medical codes are conditionally dependent on the presence of other medical codes and concepts, and wherein the method further comprises:

filtering the ranked medical codes wherein only the medical codes having a relevance score above a predetermined threshold value are displayed.

3. The method of claim 1 , wherein the multiple data sources include one of lab results, problem lists, medication lists, radiology reports, pathology reports, operation reports, and admission and discharge notes.

4. The method of claim 1 , wherein assigning the relevance score includes applying a weighing factor based on the presence of a second medical code within the one or more medical codes generated by the reasoning outcome.

5. The method of claim 4 , wherein the weighing factor is based on a lookup table including relevant combinations of medical codes; and

wherein the lookup table has a maximum size of N 2 , where N is the number of medical codes in the background ontology.

6. The method of claim 1 , wherein the one or more medical codes are based on medical ontology comprising Systematic Nomenclature of Medicine and International Classification of Diseases.

7. The method of claim 1 , further comprising converting the one or more medical codes generated by the reasoning outcome according to a predetermined medical ontology; and

wherein the color coding is based on relevance of the displayed medical code and concept.

8. The method of claim 1 , wherein displaying the medical codes in rank order includes displaying a predetermined number of medical codes.

9. The method of claim 1 , further comprising receiving a user input based on the displayed list of medical codes.

10. The method of claim 9 , wherein the user input selects one of the displayed medical codes to view one of the original data sources for the medical code and the reasoning trail for the medical code.

11. The method of claim 9 , wherein the user input filters the displayed medical codes to view medical codes from a selected data source.

12. The method of claim 1 , wherein assigning the relevance score to each of the one or more medical codes includes determining a relevance of each of the one or more medical codes relative to a context of a workflow of a user; and

wherein the color coding is based on relevance of the displayed medical code.

13. A system, comprising:

a non-transitory computer readable storage medium storing an executable program; and

a processor executing the executable program to cause the processor to:

retrieve medical data of a patient, the medical data including data from multiple data sources;

extract ontology and medical concepts from the medical data of the patient including free-text documents by processing natural language;

phenotype, using a machine learning model, the medical data of the patient and the ontology and medical concepts to generate a reasoning trail and reasoning outcome including one or more medical codes and concepts and convert the one or more medical codes and concepts to a predetermined medical ontology, the reasoning trail including a basis for which the reasoning outcome is determined;

when all of the one or more concepts and medical codes are not in the same ontology, map medical codes and concepts onto a medical vocabulary or ontology from one ontology to another;

assign a relevance score to each of the one or more medical codes and concepts;

rank the medical codes and concepts based on the relevance score of each of the one or more medical codes and concepts;

color code at least one of the ranked medical codes or concepts; and

display the medical codes and concepts in rank order.

14. The system of claim 13 , wherein the processor executes the executable program to cause the processor to:

extract medical concepts from data sources including free-text documents by processing natural language with sentence boundary detection and, wherein phenotyping the medical data includes analyzing the medical concepts.

15. The system of claim 13 , wherein the processor executes the executable program to cause the processor to:

filter the ranked medical codes, by selecting only the medical codes having a relevance score above a predetermined threshold value to be displayed.

16. The system of claim 13 , wherein assigning the relevance score includes applying a weighing factor based on the presence of a second medical code within the one or more medical codes generated by the reasoning outcome.

17. The system of claim 13 , wherein the processor executes the executable program to cause the processor to:

convert the one or more medical codes generated by the reasoning outcome according to a predetermined medical ontology.

18. The system of claim 13 , wherein the processor executes the executable program to cause the processor to:

receive a user input based on the displayed list of medical codes.

19. The system of claim 18 , wherein the user input one of (a) selects one of the displayed medical codes to view one of the original data sources for the medical code and the reasoning trail for the medical code and (b) filters the displayed medical codes to view medical codes from a selected data source.

20. The system of claim 18 , wherein assigning the relevance score to each of the one or more medical codes includes determining a relevance of each of the one or more medical codes relative to a context of a workflow of a user.

21. A non-transitory computer-readable storage medium including a set of instructions executable by a processor, the set of instructions, when executed by the processor, causing the processor to perform operations, comprising:

retrieving medical data of a patient, the medical data including data from multiple data sources;

phenotyping, using a machine learning model, the medical data to generate a reasoning trail and reasoning outcome including one or more medical codes, the reasoning trail including a basis for which the reasoning outcome is determined;

assigning a relevance score to each of the one or more medical codes;

ranking the medical codes based on the relevance score of each of the one or more medical codes;

color coding at least one of the ranked medical codes or concepts; and

displaying the medical codes in rank order.