SYSTEMS AND METHODS FOR NATURAL LANGUAGE PROCESSING-BASED CLASSIFICATION OF ELECTRONIC MEDICAL RECORDS
Systems and methods of the present disclosure enable improved natural language processing of patient-related medical information for clinical decision support. To do so, a processor receives patient data including a written report, and accesses a dictionary of terminology associated with a disease. The terminology includes descriptors indicative of categories of the disease. The processor inputs the written report into a tokenization function to output tokens by parsing word patterns in the written report, and generating the tokens from the word patterns. The processor determines a presence in the written report of each descriptor based on the tokens and determines a category-specific score associated with each category based on the presence of the descriptors. The processor determines a category recommendation score indicative of a particular category based on the category-specific scores and generates a category recommendation representing the particular category based on the category recommendation score.
1 . A method, comprising:
receiving, by at least one processor, patient data comprising at least one written report associated with a patient;
accessing, by the at least one processor, a dictionary of terminology associated with a disease;
wherein the terminology comprises a plurality of descriptors;
wherein each descriptor is indicative of at least one category of a plurality of categories associated with the disease;
wherein each descriptor is associated with at least one descriptor-specific score representative of a relevance to the at least one category;
inputting, by the at least one processor, the at least one written report into a tokenization function to output a set of tokens, wherein the tokenization function is configured to:
parse at least one word pattern in the at least one written report, and
generate the set of tokens from the at least one word pattern;
determining, by the at least one processor, at least one identified descriptor, from the plurality of descriptors, that is present in the at least one written report based at least in part on the set of tokens associated with the at least one written report;
determining, by the at least one processor, a category-specific score of a plurality of category-specific scores associated with each category of the plurality of categories based at least in part on:
the at least one identified descriptor of the plurality of descriptors and
the at least one descriptor-specific score of each descriptor representative of the relevance of each descriptor to the at least one category;
determining, by the at least one processor, at least one recommended category score indicative of at least one particular category of the disease based at least in part on the category-specific score associated with each category;
generating, by the at least one processor, at least one category recommendation representing the at least one particular category for the at least one patient based at least in part on the at least one recommended category score; and
rendering, by the at least one processor, an output to a graphical user interface (GUI) associated with the at least one patient to present to a user at least one category recommendation associated with the at least one category recommendation score so as to provide clinical decision support,
wherein the GUI is rendered with at least one filter user interface (UI) element having a plurality of selections associated with the plurality of categories of the disease;
wherein each selection of the plurality of selections is configured to cause the GUI of the display to selectively show an associated one of the plurality of categories of the disease; and
wherein user selection, via the GUI, of a respective selection associated with the at least one category recommendation causes the GUI to present the at least one written report associated with the patient.
2 . The method of claim 1 , further comprising:
inputting, by the at least one processor, each descriptor of the plurality of descriptors into the tokenization function to output a set of descriptor tokens; and
searching, by the at least one processor, the set of tokens with the set of descriptor tokens to identify occurrences of each descriptor based at least in part on a match of at least one descriptor token of each descriptor to at least one token of the written report.
3 . The method of claim 1 , wherein the plurality of descriptors comprise a plurality of combinations of a plurality of descriptor tokens, wherein each combination of the plurality of combinations represents a particular descriptor of the plurality of descriptors.
4 . The method of claim 3 , wherein the plurality of combinations and the plurality of descriptor tokens are hand-crafted.
5 . The method of claim 1 , wherein the at least one disease comprises mitral regurgitation.
6 . The method of claim 5 , wherein the plurality of categories associated with mitral regurgitation comprises:
a primary degenerative mitral regurgitation category indicative of primary degenerative mitral regurgitation, and
a secondary mitral regurgitation category indicative of secondary mitral regurgitation; and
wherein the plurality of category-specific scores associated with mitral regurgitation comprises:
a primary degenerative mitral regurgitation score associated with the primary degenerative mitral regurgitation category, and
a secondary mitral regurgitation score associated with the secondary mitral regurgitation category.
7 . The method of claim 6 , further comprising:
determining the at least one category recommendation as mixed mitral regurgitation indicative of a combination of the primary degenerative mitral regurgitation and the secondary mitral regurgitation based at least in part on the primary degenerative mitral regurgitation score indicating a presence of primary degenerative mitral regurgitation and the secondary mitral regurgitation score indicating a presence of secondary mitral regurgitation.
8 . The method of claim 6 , further comprising:
determining the at least one category recommendation as unknown mitral regurgitation based at least in part on the primary degenerative mitral regurgitation score not indicating a presence of primary degenerative mitral regurgitation and the secondary mitral regurgitation score not indicating a presence of secondary mitral regurgitation.
9 . The method of claim 1 , further comprising:
determining, by the at least one processor, at least one category recommendation score indicative of the at least one particular category based at least in part on:
the category-specific score associated with each category, and
at least one medical test result.
10 . The method of claim 1 , further comprising:
determining, by the at least one processor, at least one category recommendation score indicative of the at least one particular category based at least in part on:
the category-specific score associated with each category, and
a set of classification parameters for balancing each category-specific score so as to select a particular category of the plurality of categories.
11 . A system comprising:
at least one processor in communication with at least one non-transitory computer readable medium having software instructions stored thereon, wherein, upon execution of the software instructions, the at least one processor is configured to:
receiving, by at least one processor, patient data comprising at least one written report associated with a patient;
access a dictionary of terminology associated with a disease;
wherein the terminology comprises a plurality of descriptors;
wherein each descriptor is indicative of at least one category of a plurality of categories associated with the disease;
wherein each descriptor is associated with at least one descriptor-specific score representative of a relevance to the at least one category;
input the at least one written report into a tokenization function to output a set of tokens, wherein the tokenization function is configured to:
parse at least one word pattern in the at least one written report, and
generate the set of tokens from the at least one word pattern;
determine at least one identified descriptor, from the plurality of descriptors, that is present in the at least one written report based at least in part on the set of tokens associated with the at least one written report;
determine a category-specific score of a plurality of category-specific scores associated with each category of the plurality of categories based at least in part on:
the at least one identified descriptor of the plurality of descriptors and
the at least one descriptor-specific score of each descriptor representative of the relevance of each descriptor to the at least one category;
determine at least one recommended category score indicative of at least one particular category of the disease based at least in part on the category-specific score associated with each category;
generate at least one category recommendation representing the at least one particular category for the at least one patient based at least in part on the at least one recommended category score; and
render an output to a graphical user interface (GUI) associated with the at least one patient to present to a user at least one category recommendation associated with the at least one category recommendation score so as to provide clinical decision support,
wherein the GUI is rendered with at least one filter user interface (UI) element having a plurality of selections associated with the plurality of categories of the disease;
wherein each selection of the plurality of selections is configured to cause the GUI of the display to selectively show an associated one of the plurality of categories of the disease; and
wherein user selection, via the GUI, of a respective selection associated with the at least one category recommendation causes the GUI to present the at least one written report associated with the patient.
12 . The system of claim 11 , wherein, upon execution of the software instructions, the at least one processor is further configured to:
input each descriptor of the plurality of descriptors into the tokenization function to output a set of descriptor tokens; and
search the set of tokens with the set of descriptor tokens to identify occurrences of each descriptor based at least in part on a match of at least one descriptor token of each descriptor to at least one token of the written report.
13 . The system of claim 11 , wherein the plurality of descriptors comprise a plurality of combinations of a plurality of descriptor tokens, wherein each combination of the plurality of combinations represents a particular descriptor of the plurality of descriptors.
14 . The system of claim 13 , wherein the plurality of combinations and the plurality of descriptor tokens are hand-crafted.
15 . The system of claim 11 , wherein the at least one disease comprises mitral regurgitation.
16 . The system of claim 15 , wherein the plurality of categories associated with mitral regurgitation comprises:
a primary degenerative mitral regurgitation category indicative of primary degenerative mitral regurgitation, and
a secondary mitral regurgitation category indicative of secondary mitral regurgitation; and
wherein the plurality of category-specific scores associated with mitral regurgitation comprises:
a primary degenerative mitral regurgitation score associated with the primary degenerative mitral regurgitation category, and
a secondary mitral regurgitation score associated with the secondary mitral regurgitation category.
17 . The system of claim 16 , wherein, upon execution of the software instructions, the at least one processor is further configured to:
determine the at least one category recommendation as mixed mitral regurgitation indicative of a combination of the primary degenerative mitral regurgitation and the secondary mitral regurgitation based at least in part on at least one of:
the primary degenerative mitral regurgitation score indicating a presence of primary degenerative mitral regurgitation and the secondary mitral regurgitation score indicating a presence of secondary mitral regurgitation, or
18 . The system of claim 16 , wherein, upon execution of the software instructions, the at least one processor is further configured to:
determine the at least one category recommendation as unknown mitral regurgitation based at least in part on the primary degenerative mitral regurgitation score not indicating a presence of primary degenerative mitral regurgitation and the secondary mitral regurgitation score not indicating a presence of secondary mitral regurgitation.
19 . The system of claim 11 , wherein, upon execution of the software instructions, the at least one processor is further configured to:
determine at least one category recommendation score indicative of the at least one particular category based at least in part on:
the category-specific score associated with each category, and
at least one medical test result.
20 . The system of claim 11 , wherein, upon execution of the software instructions, the at least one processor is further configured to:
determine at least one category recommendation score indicative of the at least one particular category based at least in part on:
the category-specific score associated with each category, and
a set of classification parameters for balancing each category-specific score so as to select a particular category of the plurality of categories.