Inquiry recommendation for medical diagnosis

Patent metadata

Field	Value
Publication number	US-2022208373-A1
Application number	US-202017139014-A
Country	US
Kind code	A1
Filing date	Dec 31, 2020
Priority date	Dec 31, 2020
Publication date	Jun 30, 2022
Grant date	—

Abstract

A machine-guided inquiry recommendation for medical diagnosis. Generate a knowledge graph data structure using one or more electronic medical guideline documents. Evaluate a likelihood value for one or more diseases based on the knowledge graph data structure. Generate a best next inquiry question for use in a medical diagnosis process.

First claim

What is claimed is:

1 . A method for providing a machine-guided inquiry recommendation for medical diagnosis, comprising:
generating a best next inquiry question, for use in a medical diagnosis process, wherein the generating is based on one or more likelihood values that a set of input symptoms exhibits one or more diseases, and wherein the likelihood values are calculated using a knowledge graph data structure generated using one or more electronic medical guideline documents.

2 . The method of claim 1 , further comprising:
generating the knowledge graph data structure using one or more electronic medical guideline documents.

3 . The method of claim 1 , wherein calculating the one or more likelihood values comprises:
applying a combined term frequency-inverse document frequency (TF-IDF) process and Bayesian modelling process to one or more diseases in the knowledge graph data structure.

4 . The method of claim 3 , wherein the applying comprises:
applying the combined TF-IDF process and Bayesian modeling process to obtain an acceptable performance value; optimizing weight values of symptoms used in the applying; and applying further Bayesian modelling to improve accuracy of the one or more likelihood values.

5 . The method of claim 1 , wherein generating a best next inquiry is performed using an index.

6 . The method of claim 1 , wherein calculating the one or more likelihood values comprises:
performing feature recognition using an object-oriented symptom modeling process; performing feature selection on recognized features to infer possible features; performing feature prioritization on selected features using an information retrieval process; performing a similarity analysis on prioritized features to calculate distance of prioritized features; and generating a list of suspected diseases based on the similarity analysis.

7 . A method for providing a machine-guided inquiry recommendation for medical diagnosis, comprising:
detecting a set of factual nodes in a knowledge graph data structure; inferring a set of evidential nodes in the set of factual nodes; inferring a set of disease feature nodes in the set of evidential nodes; and inferring a set of possible disease nodes in the set of diseases feature nodes.

8 . The method of claim 7 , wherein detecting a set of factual nodes in a knowledge graph data structure comprises:
measuring, using semantic matching, a similarity between an input text one or more nodes in the knowledge graph data structure, the input text comprising one or more observed symptoms, wherein the measuring yields a confidence score.

9 . The method of claim 7 , wherein inferring a set of evidential nodes in the set of factual nodes comprises:
identifying one or more candidate evidential nodes among the set of factual nodes, wherein the identifying is based on hyponymy relationships between at least two factual nodes in the set of factual nodes and is further based on a confidence value for each of the one or more candidate evidential nodes; adding to the set of inferred set of evidential nodes, at least one of the one or more candidate evidential nodes based on its corresponding confidence value, the at least one factual node as an inferred evidential node.

10 . The method of claim 7 , wherein inferring a set of disease feature nodes in the set of evidential nodes comprises:
determining a set of disease feature nodes in the set of evidential nodes; and inferring a set of possible disease nodes in the set of diseases feature nodes.

11 . A computer program product for providing a machine-guided inquiry recommendation for medical diagnosis, comprising one or more tangible storage media storing programming instructions for execution by one or more processors of one or more computer systems, the programming instructions comprising instructions for:
generating, by the one or more processors, a best next inquiry question, for use in a medical diagnosis process, wherein the generating is based on one or more likelihood values that a set of input symptoms exhibits one or more diseases, and wherein the likelihood values are calculated using a knowledge graph data structure generated using one or more electronic medical guideline documents.

12 . The computer programming product of claim 11 , further comprising instructions for:
generating, by the one or more processors, the knowledge graph data structure using one or more electronic medical guideline documents.

13 . The computer programming product of claim 11 , wherein calculating the one or more likelihood values comprises:
applying, by the one or more processors, a combined term frequency-inverse document frequency (TF-IDF) process and Bayesian modelling process to one or more diseases in the knowledge graph data structure.

14 . The computer program product of claim 13 , wherein the applying comprises:
applying, by the one or more processors, the combined TF-IDF process and Bayesian modeling process to obtain an acceptable performance value; optimizing, by the one or more processors, weight values of symptoms used in the applying; and applying, by the one or more processors, further Bayesian modelling to improve accuracy of the one or more likelihood values.

15 . The computer programming product of claim 14 , wherein generating a best next inquiry is performed using an index.

16 . The computer programming product of claim 11 , wherein calculating the one or more likelihood values comprises:
performing, by the one or more processors, feature recognition using an object-oriented symptom modeling process; performing, by the one or more processors, feature selection on recognized features to infer possible features; performing, by the one or more processors, feature prioritization on selected features using an information retrieval process; performing, by the one or more processors, a similarity analysis on prioritized features to calculate distance of prioritized features; and generating, by the one or more processors, a list of suspected diseases based on the similarity analysis.

Assignees

• IBM

Inventors

• Dang Ya Bin
• Wang Ke
• Wang Jian
• Jiang Jian Min

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/041

  Abduction · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G16H70/40

  relating to drugs, e.g. their side effects or intended usage · CPC title

• G16H50/20 Primary

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title