DECISION-SUPPORT APPLICATION AND SYSTEM FOR PROBLEM SOLVING USING A QUESTION-ANSWERING SYSTEM

• US 20120078837A1
• Filed: 03/31/2011
• Published: 03/29/2012
• Est. Priority Date: 09/24/2010
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bagchi, Sugato, Ferrucci, David A., Levas, Anthony T., Mueller, Erik T.
• Granted Patent: US 9,002,773 B2
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: G06F 16/2428 Query predicate definition using graphical user interfaces, including menus and forms G06F16/2423 takes precedence
• CPC Class Codes: G06F 16/24522 Translation of natural language queries to structured queries
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/31 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/532 Query formulation, e.g. graphical querying
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 3/048 Interaction techniques based on graphical user interfaces [GUI]
• CPC Class Codes: G06F 40/134 Hyperlinking
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06N 5/027 Frames
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/00 : ICT specially adapted for the handling or processing of medical references
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A decision-support system for problem solving comprises software modules embodied on a computer readable medium, and the software modules comprise an input/output module and a question-answering module. The method receives problem case information using the input/output module, generates a query based on the problem case information, and generates a plurality of answers for the query using the question-answering module. The method also calculates numerical values for multiple evidence dimensions from evidence sources for each of the answers using the question-answering module and calculates a corresponding confidence value for each of the answers based on the numerical value of each evidence dimension using the question-answering module. Further, the method outputs the answers, the corresponding confidence values, and the numerical values of each evidence dimension for one or more selected answers using the input/output module.

Claims

1. A method comprising:

providing a decision-support system for problem solving, said system comprising software modules embodied on a computer readable medium, and said software modules comprising an input/output module and a question-answering module;

receiving problem case information using said input/output module;

generating a query based on said problem case information;

generating a plurality of answers for said query using said question-answering module;

calculating numerical values for multiple evidence dimensions from evidence sources for each of said answers using said question-answering module;

calculating a corresponding confidence value for each of said answers based on said numerical value of each evidence dimension using said question-answering module;

outputting said answers, said corresponding confidence values, and said numerical values of each evidence dimension for one or more selected answers using said input/output module.

2. The method according to claim 1, wherein said decision-support system further comprises a problem case analysis module and question generation module, and wherein said step of generating said query is carried out by steps comprising:

automatically analyzing said problem case information, using said problem case analysis module, in order to identify semantic concepts; and

automatically generating said query from said semantic concepts using said question generation module.

3. The method according to claim 2, wherein said steps of generating said query, generating said plurality of answers, calculating said numerical values for multiple evidence dimensions from evidence sources for each of said answers, and calculating said confidence value for each of said answers based on said numerical value of each evidence dimension are carried out automatically if new problem case information is received.

4. The method according to claim 3, further comprising:

storing, in a repository, said plurality of answers, numerical values and confidence values generated automatically if new problem case information is received.

5. The method according to claim 1, wherein the step of generating said plurality of answers is carried out by analyzing domain knowledge content.

6. The method according to claim 5, further comprising:

storing, in a repository, said plurality of answers, numerical values and confidence values;

determining whether a plurality of previously generated answers and corresponding confidence values stored in said repository would change if new domain knowledge is added to said domain knowledge content; and

sending an alert using said input/output model if a change is determined.

7. The method according to claim 1, wherein said step of generating said query is carried out by steps comprising:

receiving said query through said input/output module in a form of at least one of a free-form query, a free-form statement, and keyword search.

8. The method according to claim 7, wherein said decision-support system further comprises a problem case analysis module and question generation module, the method further comprising:

automatically analyzing said problem case information, using said problem case analysis module, in order to identify semantic concepts; and

expanding said query using said semantic concepts using said question generation module.

9. The method according to claim 1, further comprising:

outputting, for a selected evidence dimension, each piece of evidence supporting said selected evidence dimension and an associated provenance for said piece of evidence.

10. The method according to claim 9, further comprising:

removing a selected piece of evidence from said evidence sources contributing to a numerical value of an evidence dimension;

recalculating, using said question-answering module, said numerical value of said evidence dimension having said selected piece of evidence removed; and

recalculating a new confidence value of an answer based on said recalculated numerical value using said question-answering module.

11. The method according to claim 1, further comprising:

identifying, using said question-answering module, information relevant to said answers that is not contained within said problem case information as missing information; and

outputting a request to add said missing information to said problem case information.

12. The method according to claim 11, further comprising:

identifying, using said question-answering module, an amount said missing information affects corresponding confidence values of answers.

13. A system comprising:

a first repository maintaining problem case information;

a computer processor operatively connected to said first repository; and

a second repository operatively connected to said computer processor,

said computer processor configured to receive said problem case information from said first repository;

said computer processor configured to generate a query based on said problem case information;

said computer processor configured to generate a plurality of answers for said query;

said computer processor configured to calculate numerical values for multiple evidence dimensions from evidence sources for each of said answers;

said computer processor configured to calculate corresponding confidence values for each of said answers based on said numerical values of each evidence dimension; and

said computer processor configured to output said queries, said answers, said corresponding confidence values, and said numerical values of each evidence dimension to said second repository.

14. The system according to claim 13, wherein said configuration of said computer processor to generate a query based on said problem case information is carried out by configuring said computer processor to analyze said problem case information in order to identify semantic concepts and generate said query from said semantic concepts.

15. The system according to claim 14, wherein said computer processor is further configured to automatically generate said query, automatically generate said plurality of answers, automatically calculate said numerical values for multiple evidence dimensions from evidence sources for each of said answers, and automatically calculate said confidence value for each of said answers based on said numerical value for each evidence dimension if new problem case information is received.

16. The system according to claim 13, further comprising a third repository for maintaining domain knowledge content, wherein said configuration of said computer processor to generate said plurality of answers is carried out by configuring said computer processor to analyze said domain knowledge content.

17. The system according to claim 16, wherein said computer processor is further configured to determine whether a plurality of previously generated answers and corresponding confidence values stored in said second repository would change if new domain knowledge is added to said domain knowledge content stored in said third repository and wherein said computer processor is further configured to send an alert if a change is determined.

18. The system according to claim 13, wherein said configuration of said computer processor to generate said query is carried out by configuring said computer processor to receive said query as an input in a form of at least one of a free-form query, a free-form statement, and keyword search.

19. The system according to claim 13, wherein said computer processor is further configured to output a piece of evidence supporting a selected evidence dimension and an associated provenance for said piece of evidence.

20. The system according to claim 19, wherein said computer processor is further configured to remove a selected piece of evidence from said evidence sources contributing to a numerical value of an evidence dimension, recalculating said numerical value of said evidence dimension having said selected piece of evidence removed, and recalculating a new confidence value of an answer based on said recalculated numerical value.

21. The system according to claim 13, wherein said computer processor is further configured to identify information relevant to said answers that is not contained within said problem case information as missing information and output a request to add said missing information to said problem case information.

22. The system according to claim 21, wherein said computer processor is further configured to identify an amount said missing information affects corresponding confidence values of answers.

23. A computer program product comprising a computer readable storage medium storing computer readable program code comprising instructions executable by a computerized device, said computer program code comprising:

an input/output module receiving problem case information;

a problem case analysis module analyzing said problem case information in order to identify semantic concepts;

a question generation module generating a query from said semantic concepts; and

a question-answering module generating a plurality of answers for said query,

said question-answering module calculating numerical values for multiple evidence dimensions from evidence sources for each of said answers,

said question-answering module calculating corresponding confidence values for each of said answers based on said numerical value of each evidence dimension using said question-answering module, and

said input/output module outputting said queries, said answers, said corresponding confidence values, and said numerical values for multiple evidence dimensions.