Automatically generating reasoning graphs
Embodiments disclosed herein relate to methods and systems for transliterating reasoning graphs and using the same to determine insights.
1. A method of automatically outputting transliterations of a target reasoning graph, the method comprising:
inputting a target reasoning graph into a computer, wherein the target reasoning graph includes:
a plurality of leaf nodes each defining an insight;
a plurality of reasoning paths each terminating at a leaf node, and
a plurality of internal nodes each describing a reasoning function in plain language format, wherein each reasoning function defines a portion of the plurality of reasoning paths and defines queries and inputs for making a discrete decision with reference data at a specific point along a selected reasoning path;
wherein the plurality of internal nodes, the plurality of leaf nodes, and the plurality of reasoning functions of the target reasoning graph are in a plain language format;
providing, in the computer, a transliteration library including:
a portfolio of templating functions in a templating language;
a portfolio of reasoning functions in the plain language format;
wherein the transliteration library defines a correspondence between each reasoning function of the portfolio of reasoning functions and a selected templating function of the portfolio of templating functions;
with the computer, automatically generating a templating language representation of the target reasoning graph by correlating the reasoning functions of the target reasoning graph with the templating functions of the portfolio of templating functions corresponding thereto; and
outputting the templating language representation of the target reasoning graph.
2. The method of claim 1 , wherein the templating language representation of the target reasoning graph includes a plurality of target reasoning functions from the portfolio of reasoning functions represented as templating functions of the target reasoning graph.
3. The method of claim 1 , further comprising:
with the computer, automatically generating a documentation language representation of the target reasoning graph by converting the templating language representation of the target reasoning graph into one or more of CSS, HTML, Javascript, or XML; and
displaying the documentation language representation of the target reasoning graph on a screen.
4. The method of claim 1 , further comprising:
with the computer, automatically generating a documentation language representation of the target reasoning graph by converting the templating language representation of the target reasoning graph another format; and
displaying the documentation language representation of the target reasoning graph after the converting.
5. The method of claim 1 , wherein inputting a target reasoning graph into a computer includes inputting the target reasoning graph in data exchange format.
6. The method of claim 5 , wherein the data exchange format includes one or more of JSON or XML format.
7. The method of claim 6 , further comprising converting the templating language representation of the target reasoning graph into one or more high-level programming language representations of the target reasoning graph, with a compiler.
8. The method of claim 7 , further comprising converting the one or more high-level programming language representations of the target reasoning graph to an assembly language representation of the target reasoning graph, with a standard compiler.
9. The method of claim 8 , further comprising converting the assembly language representation of the target reasoning graph into machine executable code with an additional compiler.
10. The method of claim 1 , further comprising converting the templating language representation of the target reasoning graph into one or more high-level programming languages.
11. The method of claim 10 , wherein of converting the templating language representation of the target reasoning graph into one or more high-level programming languages includes converting the templating language into one or more of C, C++, Clojure, Go, Java, Javascript, Python, Ruby, or Scala, with a compiler.
12. The method of claim 1 , wherein the templating language representation of the target reasoning graph includes one or more links to the reference data for defining inputs in one or more of the reasoning functions.
13. The method of claim 12 , wherein the reference data defines one or more of the inputs for making one or more of the discrete decisions.
14. A method of automatically outputting transliterations of a target reasoning graph, the method comprising:
inputting a target reasoning graph into a computer in data exchange format, wherein the target reasoning graph includes:
a plurality of leaf nodes each defining an insight;
a plurality of reasoning paths each terminating at a leaf node, and
a plurality of internal nodes each describing a reasoning function in plain language format, wherein each reasoning function defines a portion of the plurality of reasoning paths and defines queries and inputs for making a discrete decision with reference data at a specific point along a selected reasoning path;
wherein the plurality of internal nodes, the plurality of leaf nodes, and the plurality of reasoning functions of the target reasoning graph are in a plain language format;
providing, in the computer, a transliteration library including:
a portfolio of templating functions in a templating language;
a portfolio of reasoning functions in the plain language format;
wherein the transliteration library defines a correspondence between each reasoning function of the portfolio of reasoning functions and a selected templating function of the portfolio of templating functions;
with the computer, automatically generating a templating language representation of the target reasoning graph by correlating the reasoning functions of the target reasoning graph with the templating functions of the portfolio of templating functions corresponding thereto;
with a first compiler, converting the templating language representation of the target reasoning graph into one or more of a high-level programming language representation or a documentation language representation of the target reasoning graph; and
with a second compiler, converting the high-level programming language or the documentation language representation of the target reasoning graph to machine executable code.
15. The method of claim 14 , wherein converting the templating language representation of the target reasoning graph into one or more of a high-level programming language representation or a documentation language representation of the target reasoning graph includes converting the templating language representation of the target reasoning graph to one or more of C, C++, Clojure, Go, Java, Javascript, Python, Ruby, or Scala.
16. The method of claim 14 , wherein converting the high-level programming language or the documentation language representation of the target reasoning graph to machine executable code includes converting the high-level programming language representation of the target reasoning graph into assembly language and converting the assembly language into machine executable code.
17. The method of claim 14 , wherein converting the high-level programming language or the documentation language representation of the target reasoning graph to machine executable code includes converting the documentation language representation of the target reasoning graph to one or more of CSS, HTML, Javascript, or XML.
18. The method of claim 17 , further comprising displaying the documentation language representation of the target reasoning graph on a web page.
19. The method of claim 18 , wherein the documentation language representation of the target reasoning graph includes the plain language representation of the target reasoning graph on the web page.
20. A method of automatically determining an insight using a transliterated target reasoning graph, the method comprising:
inputting a target reasoning graph into a computer in data exchange format, wherein the target reasoning graph includes:
a plurality of leaf nodes each defining an insight;
a plurality of reasoning paths each terminating at a leaf node, and
a plurality of internal nodes each describing a reasoning function in plain language format, wherein each reasoning function defines a portion of the plurality of reasoning paths and defines queries and inputs for making a discrete decision with reference data at a specific point along a selected reasoning path;
wherein the plurality of internal nodes, the plurality of leaf nodes, and the plurality of reasoning functions of the target reasoning graph are in a plain language format;
providing, in the computer, a transliteration library including:
a portfolio of templating functions in a templating language;
a portfolio of reasoning functions in the plain language format;
wherein the transliteration library defines a correspondence between each reasoning function of the portfolio of reasoning functions and a selected templating function of the portfolio of templating functions;
with the computer, automatically generating a templating language representation of the target reasoning graph by correlating the reasoning functions of the target reasoning graph with the templating functions of the portfolio of templating functions corresponding thereto;
with the computer, converting the templating language representation of the target reasoning graph into one or more of a high-level programming language representation or a documentation language representation of the target reasoning graph;
with at least one compiler, converting the high-level programming language representation of the target reasoning graph to machine executable code; and
with the computer and the machine executable code of the target reasoning graph, determining an insight using the reference data to make each discrete decision corresponding to each reasoning function in the selected reasoning path until the selected reasoning path reaches a selected leaf node corresponding thereto.
21. The method of claim 20 , wherein the reference data includes one or more of look-up tables, subject specific information, National Correct Coding Initiative codes, codes representing diagnoses, amounts, or dates.
22. The method of claim 20 , wherein, with the computer and the machine executable code of the target reasoning graph, determining an insight using the reference data to make each discrete decision corresponding to each reasoning function in the selected reasoning path until the selected reasoning path reaches a selected leaf node corresponding to the selected reasoning path, includes executing the machine executable code of the target reasoning graph to determine the insight.
23. The method of claim 20 , further comprising outputting the insight.
24. The method of claim 20 , wherein the templating language representation of the target reasoning graph includes one or more links to the reference data for defining inputs in one or more of the reasoning functions of the target reasoning graph.
25. The method of claim 20 , wherein the reference data is provided in machine executable code.
26. The method of claim 20 , further comprising transliterating one or more of the insight, the selected reasoning path, the reasoning functions of the selected reasoning path, the selected leaf node, or the reference data used to determine the discrete decisions corresponding to the reasoning functions of the selected reasoning path, to a plain language representation using the transliteration library.
27. The method of claim 26 , further comprising outputting the plain language representation using the computer.
28. The method of claim 26 , further comprising transliterating the insight, the reasoning functions of the selected reasoning path, the selected leaf node, and the reference data used to determine the discrete decisions corresponding to the reasoning functions of the selected reasoning path, from the templating language to a documentation language representation using the transliteration library.
29. The method of claim 20 , wherein converting the templating language representation of the target reasoning graph into one or more of a high-level programming language representation or a documentation language representation of the target reasoning graph includes converting the templating language representation of the target reasoning graph to one or more of C, C++, HTML, Clojure, Go, Java, Javascript, Python, Ruby, Scala, or XML.
30. The method of claim 20 , wherein inputting the target reasoning graph into the computer in data exchange format includes inputting the target reasoning graph into the computer in JSON or XML format.
31. A computer program product for of automatically outputting transliterations of a target reasoning graph, the computer program product comprising:
a machine readable program stored on a non-transitory computer readable medium, the machine readable program including:
an input module configured for accepting input of a target reasoning graph in data exchange format, wherein the target reasoning graph includes:
a plurality of leaf nodes each defining an insight;
a plurality of reasoning paths each terminating at a leaf node, and
a plurality of internal nodes each describing a reasoning function in plain language format, wherein each reasoning function defines a portion of the plurality of reasoning paths and defines queries and inputs for making a discrete decision with reference data at a specific point along a selected reasoning path;
wherein the plurality of internal nodes, the plurality of leaf nodes, and the plurality of reasoning functions of the target reasoning graph are in a plain language format;
a transliteration library including:
a portfolio of templating functions in a templating language;
a portfolio of reasoning functions in the plain language format;
wherein the transliteration library defines a correspondence between each reasoning function of the portfolio of reasoning functions and a selected templating function of the portfolio of templating functions;
a transliteration module configured for automatically generating a templating language representation of the target reasoning graph by correlating the reasoning functions of the target reasoning graph with the templating functions of the portfolio of templating functions corresponding thereto;
a reference data library for storing the reference data;
a first compiler for converting the templating language representation of the target reasoning graph into one or more of a high-level programming language representation or a documentation language representation of the target reasoning graph;
a second compiler for converting the high-level programming language representation or the documentation language representation of the target reasoning graph into machine executable code; and
an output module configured for outputting the machine code.
32. The computer program product of claim 31 , wherein the input module is configured to accept input of JSON or XML data exchange formats.
33. The computer program product of claim 31 , wherein the high-level programming language representation includes one or more of C, C++, HTML, Clojure, Go, Java, Javascript, Python, Ruby, Scala, or XML.
34. The computer program product of claim 31 , wherein the documentation language representation includes one or more of CSS, HTML, Javascript, or XML.
35. The computer program product of claim 31 , wherein the reference data includes one or more of look-up tables, subject specific information, National Correct Coding Initiative codes, codes representing diagnoses, amounts, or dates.