Obtaining supported decision trees from text for medical health applications

Patent metadata

Field	Value
Publication number	US-11847411-B2
Application number	US-202117339899-A
Country	US
Kind code	B2
Filing date	Jun 4, 2021
Priority date	Jul 29, 2020
Publication date	Dec 19, 2023
Grant date	Dec 19, 2023

Abstract

Systems, devices, and methods discussed herein provide improved decision trees (e.g., supported decision trees). A supported decision tree can be generated by generating discourse trees from various documents associated with a subject. One or more decision chains can be generated from each discourse tree, each decision chain being a sequence of elements comprising a premise and a decision connected by rhetorical relationships. A supported decision tree can be generated from the various decision chains, where the nodes of the decision tree are identified from the elements of the plurality of decision chains and ordered based on a set of predefined priority rules. Subsequent input data can be received and the supported decision tree can be traversed to classify the input data.

First claim

What is claimed is:

1. A method for classifying input data using a supported decision tree, comprising:
accessing a corpus of documents associated with a subject;
generating a first discourse tree from a first document and a second discourse tree from a second document, each discourse tree including a plurality of nodes, each nonterminal node representing a rhetorical relationship between at least two fragments of a corresponding document, and each terminal node of the nodes of the discourse tree being associated with one of the fragments, the first and second documents from the corpus of documents;
generating, by the one or more processors, a first plurality of decision chains from the first discourse tree and a second plurality of decision chains from the second discourse tree, each decision chain being a sequence of elements comprising a premise and a decision connected by rhetorical relationships, the elements being identified from the plurality of nodes of the discourse trees;
generating, by the one or more processors, the supported decision tree based at least in part on the first and second plurality of decision chains, the supported decision tree having nodes that correspond to a feature of a respective decision and edges corresponding to a value of the feature, wherein the nodes of the supported decision tree are identified from the elements of the first and second plurality of decision chains and ordered based at least in part on a set of predefined priority rules;
receiving the input data; and
classifying the input data based at least in part on traversing the supported decision tree using the input data.

2. The method of claim 1 , wherein generating the first and second plurality of decision chains and the supported decision tree is performed as an offline process.

3. The method of claim 1 , further comprising:
identifying a respective premise and corresponding decision from the first discourse tree based at least in part on respective rhetorical relationships identified by the nodes of the first discourse tree; and
generating a decision chain to comprise the respective premise and the corresponding decision.

4. The method of claim 1 , further comprising:
identifying, based at least in part on a predefined ontology, a common entity of two decision chains, wherein a first of the two decision chains is included in the first plurality of decision chains and a second of the two decision chains is included in the second plurality of decision chains; and
merging the two decision chains to form a decision navigation graph, the two decision chains being merged based at least in part on the common entity, the decision navigation graph comprising respective nodes representing each respective element of the two decision chains connected by edges representing corresponding rhetorical relationships.

5. The method of claim 4 , further comprising ordering the respective nodes of the decision navigation graph to form a decision pre-tree, the decision pre-tree being a fragment of the supported decision tree, the ordering being performed in accordance with set of predefined priority rules.

6. The method of claim 5 , further comprising ordering the respective nodes of the decision navigation graph to form a second decision pre-tree, the second decision pre-tree being a second fragment of the supported decision tree.

7. The method of claim 6 , further comprising:
assigning linguistic information comprising an entity type, one or more entity attributes, and one or more rhetorical relationships to each node of the decision pre-tree and the second decision pre-tree; and
merging the decision pre-tree and the second decision pre-tree to form the supported decision tree.

8. A computing device, comprising:
one or more processors; and
one or more memories storing computer-readable instructions for classifying input data using a supported decision tree, that, when executed by the one or more processors, cause the computing device to perform operations comprising:
accessing a corpus of documents associated with a subject;
generating a first discourse tree from a first document and a second discourse tree from a second document, each discourse tree including a plurality of nodes, each nonterminal node representing a rhetorical relationship between at least two fragments of a corresponding document, and each terminal node of the nodes of the discourse tree being associated with one of the fragments, the first and second documents from the corpus of documents;
generating, by the one or more processors, a first plurality of decision chains from the first discourse tree and a second plurality of decision chains from the second discourse tree, each decision chain being a sequence of elements comprising a premise and a decision connected by rhetorical relationships, the elements being identified from the plurality of nodes of the discourse trees;
generating, by the one or more processors, the supported decision tree based at least in part on the first and second plurality of decision chains, the supported decision tree having nodes that correspond to a feature of a respective decision and edges corresponding to a value of the feature, wherein the nodes of the supported decision tree are identified from the elements of the first and second plurality of decision chains and ordered based at least in part on a set of predefined priority rules;
receiving the input data; and
classifying the input data based at least in part on traversing the supported decision tree using the input data.

9. The computing device of claim 8 , wherein generating the first and second plurality of decision chains and the supported decision tree is performed as an offline process.

10. The computing device of claim 8 , wherein executing the instructions by the one or more processors, further causes the computing device to perform operations comprising:
identifying a respective premise and corresponding decision from the first discourse tree based at least in part on respective rhetorical relationships identified by the nodes of the first discourse tree; and
generating a decision chain to comprise the respective premise and the corresponding decision.

11. The computing device of claim 8 , wherein executing the instructions by the one or more processors, further causes the computing device to perform operations comprising:
identifying, based at least in part on a predefined ontology, a common entity of two decision chains, wherein a first of the two decision chains is included in the first plurality of decision chains and a second of the two decision chains is included in the second plurality of decision chains; and
merging the two decision chains to form a decision navigation graph, the two decision chains being merged based at least in part on the common entity, the decision navigation graph comprising respective nodes representing each respective element of the two decision chains connected by edges representing corresponding rhetorical relationships.

12. The computing device of claim 11 , wherein executing the instructions by the one or more processors, further causes the computing device to perform operations comprising ordering the respective nodes of the decision navigation graph to form a decision pre-tree, the decision pre-tree being a fragment of the supported decision tree, the ordering being performed in accordance with set of predefined priority rules.

13. The computing device of claim 12 , wherein executing the instructions by the one or more processors, further causes the computing device to perform operations comprising ordering the respective nodes of the decision navigatio

Assignees

• Oracle Int Corp

Inventors

• Galitsky Boris

Classifications

• G06F40/20 Primary

  Natural language analysis (semantic analysis of natural language G06F40/30) · CPC title

• G06F16/9027

  Trees · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G16H50/30 Primary

  for calculating health indices; for individual health risk assessment · CPC title

• G06F40/30 Primary

  Semantic analysis · CPC title