System and method for providing health care advice by diagnosing system function

• US 20030140063A1
• Filed: 12/17/2002
• Published: 07/24/2003
• Est. Priority Date: 12/17/2001
• Status: Abandoned Application

• Current Assignee: Salugenecists, Incorporated
• Original Assignee: Salugenecists, Incorporated
• Inventors: Levin, Buck, Pizzorno, Joseph E., Evans, Arnold K.
• Application Number:
  US10/322,282
• Publication Number:
  US 20030140063A1
• Time in Patent Office: Days
• US Class Current: 707/104.1
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A personalized health care advice system and method are provided wherein the system incorporates an expert system for generated smart questions which are used to determine the appropriate course of action and disease diagnosis. In a preferred embodiment, the system uses probabilistic analysis to determine a diagnosis.

Claims

1. A computer implemented system for generating personalized health care recommendations to correct physiological dysfunction, the system comprising:

a first computer having a processor that executes a health care expert system and a database for storing one or more pieces of health care information used by the expert system to generate health care recommendations;

a second computer connected to the first computer by a computer network, the second computer further comprising a processor that executes a software application for receiving a user interface from the first computer over the computer network; and

wherein the second computer communicates user related health information to the first computer using the user interface and the expert system generates one or more health care recommendations for the user based on the user related health information.

2. The system of claim 1, wherein the expert system generates one or more different types of health care recommendations and a probability of efficacy for each health care recommendation and wherein all of the health care recommendations are ranked, independent of intervention type, according to the probability of efficacy.

3. The system of claim 1, wherein the expert system further comprises one or more nodes and one or more links between the nodes wherein the nodes contain a piece of evidence and wherein each link from a first node to a second node indicates the probability of the piece of evidence in the first node indicating the piece of evidence in the second node.

4. The system of claim 3, wherein the one or more nodes further comprise one or more input nodes, one or more damaging factor nodes, one or more concept nodes, one or more endnode nodes and one or more intervention nodes;

and

wherein each input node comprises a piece of health information input into the expert system, each damaging factor node comprises a cause of dysfunction, each concept node comprises an abstract dysfunction of a user, each endnode node comprises a dysfunction of a user at a physiological level and each intervention node comprises an intervention to repair a dysfunction of the user.

5. The system of claim 4, wherein the one or more nodes further comprises one or more composite nodes wherein each composite node comprises an action to be taken based on two or more pieces of health information inputs.

6. The system of claim 3, wherein the probability associated with each link further comprises a true positive probability and a false positive probability.

7. The system of claim 6, wherein each node further comprises an apriori probability of being true.

8. The system of claim 7, wherein each node generates a probability of the node being true wherein the probability is based on the true positive and false positive probabilities of the link to the node and the apriori probability associated with the node.

9. The system of claim 3, wherein each node generates a probability of the dysfunction contained in the node wherein the probability is Boolean.

10. The system of claim 3, wherein the nodes and links of the expert system are stored in the database as one or more database tables.

11. The system of claim 10, wherein the database further comprises one or more tables that store health care related literature.

12. The system of claim 1, wherein the recommendations further comprises a health status of the user, a dosage recommendation and a literature recommendation.

13. The system of claim 3, wherein each node generates one or more smart questions based on the information provided from the user, the smart questions generated in order to gather more information about the user.

14. The system of claim 13, wherein the expert system prioritizes the smart questions generated by the nodes of the expert system based on the probability that the question will lead to further information about the user.

15. The system of claim 8, wherein each node probability further comprises

Pd|f=Pf*Pa*tp/(Pa*tp+(1−

Pa)*fp)

where Pf is the apriori probability of the “

finding”

or input node, tp and fp are the true and false positive fractions, Pa is the apriori probability and Pd|f is the posterior probability given the “

finding”

for the current node.

16. The system of claim 4, wherein the nodes further comprise an interactions node which identifies interactions between recommendations and drug therapy wherein the interactions node identifies drug therapy side-effects and identifies nutrients to counteract the drug therapy side-effects.

17. A computer implemented method for generating personalized health care recommendations to correct physiological dysfunction using a health care expert system and a database for storing one or more pieces of health care information used by the expert system to generate personalized health care recommendations, the method comprising:

receiving a user interface;

communicating user related health information to the computer using the user interface; and

generating health care recommendations using the health care expert system for the user based on the user related health information.

18. The method of claim 17, wherein the expert system generates one or more different types of health care recommendations and a probability of efficacy for each health care recommendation and wherein all of the health care recommendations are ranked, independent of intervention type, according to the probability of efficacy.

19. The method of claim 17, wherein the expert system further comprises one or more nodes and one or more links between the nodes wherein the nodes contain a piece of evidence and wherein each link from a first node to a second node indicates the probability of the piece of evidence in the first node indicating the piece of evidence in the second node.

20. The method of claim 19, wherein the one or more nodes further comprise one or more input nodes, one or more damaging factor nodes, one or more concept nodes, one or more endnode nodes and one or more intervention nodes;

and

wherein each input node comprises a piece of health information input into the expert system, each damaging factor node comprises a cause of dysfunction, each concept node comprises an abstract dysfunction of a user, each endnode node comprises a dysfunction of a user at a physiological level and each intervention node comprises an intervention to repair a dysfunction of the user.

21. The method of claim 20, wherein the one or more nodes further comprises one or more composite nodes wherein each composite node comprises an action to be taken based on two or more pieces of health information inputs.

22. The method of claim 19, wherein the probability associated with each link further comprises a true positive probability and a false positive probability.

23. The method of claim 22, wherein each node further comprises an apriori probability of a node being true.

24. The method of claim 23, wherein each node generates a probability of the node being true wherein the probability is based on the true positive and false positive probabilities of the link to the node and the apriori probability associated with the node.

25. The method of claim 19, wherein each node generates a probability of the node wherein the probability is Boolean.

26. The method of claim 19, wherein the nodes and links of the expert system are stored in the database as one or more database tables.

27. The method of claim 26, wherein the database further comprises one or more tables that store health care related literature.

28. The method of claim 17, wherein the recommendations further comprises a health status of the user, a dosage recommendation and a literature recommendation.

29. The method of claim 19, wherein each node generates one or more smart questions based on the information provided from the user, the smart questions generated in order to gather more information about the user.

30. The method of claim 29, wherein the expert system prioritizes the smart questions generated by the nodes of the expert system based on the probability that the question will lead to further information about the user.

31. The method of claim 24, wherein each node probability further comprises

Pd|f=Pf*Pa*tp/(Pa*tp+(1−

Pa)*fp)

where Pf is the apriori probability of the “

finding”

or input node, tp and fp are the true and false positive fractions, Pa is the apriori probability and Pd|f is the posterior probability given the “

finding”

for the current node.

32. The method of claim 20, wherein the nodes further comprise an interactions node which identifies interactions between recommendations wherein the interactions node identifies drug therapy side-effects and identifies nutrients to counteract the drug therapy side-effects.

33. A computer implemented system for generating personalized health care recommendations to correct physiological dysfunction, the system contained in one or more instructions and being executed by a processor of a computer system, the system comprising:

a health care expert system;

a database for storing one or more pieces of health care information used by the expert system to generate health care recommendations; and

one or more instructions that receive user related health information and one or more instructions that generate health care recommendations for the user based on the user related health information.

34. The system of claim 33, wherein the expert system generates one or more different types of health care recommendations and a probability of efficacy for each health care recommendation and wherein all of the health care recommendations are ranked, independent of intervention type, according to the probability of efficacy.

35. The system of claim 33, wherein the expert system further comprises one or more nodes and one or more links between the nodes wherein the nodes contain a piece of evidence and wherein each link from a first node to a second node indicates the probability of the piece of evidence in the first node indicating the piece of evidence in the second node.

36. The system of claim 35, wherein the one or more nodes further comprise one or more input nodes, one or more damaging factor nodes, one or more concept nodes, one or more endnode nodes and one or more intervention nodes;

and

wherein each input node comprises a piece of health information input into the expert system, each damaging factor node comprises a cause of dysfunction, each concept node comprises an abstract dysfunction of a user, each endnode node comprises a dysfunction of a user at a physiological level and each intervention node comprises an intervention to repair a dysfunction of the user.

37. The system of claim 36, wherein the one or more nodes further comprises one or more composite nodes wherein each composite node comprises an action to be taken based on two or more pieces of health information inputs.

38. The system of claim 35, wherein the probability associated with each link further comprises a true positive probability and a false positive probability.

39. The system of claim 38, wherein each node further comprises an apriori probability of being true.

40. The system of claim 39, wherein each node generates a probability of the node being true wherein the probability is based on the true positive and false positive probabilities of the link to the node and the apriori probability associated with the node.

41. The system of claim 35, wherein each node generates a probability of the node wherein the probability is Boolean.

42. The system of claim 35, wherein the nodes and links of the expert system are stored in the database as one or more database tables.

43. The system of claim 42, wherein the database further comprises one or more tables that store health care related literature.

44. The system of claim 33, wherein the recommendations further comprises a health status of the user, a dosage recommendation and a literature recommendation.

45. The system of claim 35, wherein each node generates one or more smart questions based on the information provided from the user, the smart questions generated in order to gather more information about the user.

46. The system of claim 45, wherein the expert system prioritizes the smart questions generated by the nodes of the expert system based on the probability that the question will lead to further information about the user.

47. The system of claim 40, wherein each node probability further comprises

Pd|f=Pf*Pa*tp/(Pa*tp+(1−

Pa)*fp)

where Pf is the apriori probability of the “

finding”

or input node, tp and fp are the true and false positive fractions, Pa is the apriori probability and Pd|f is the posterior probability given the “

finding”

for the current node.

48. The system of claim 36, wherein the nodes further comprise an interactions node which identifies interactions between recommendations wherein the interactions node identifies drug therapy side-effects and identifies nutrients to counteract the drug therapy side-effects.

49. A computer-implemented personalized health care expert system, comprising:

one or more input nodes wherein each input node comprises a piece of health information input into the expert system;

one or more damaging factor nodes wherein each damaging factor node comprises a cause of dysfunction;

one or more concept nodes wherein each concept node comprises an abstract dysfunction of a user;

one or more endnode nodes wherein each endnode node comprises a dysfunction of a user at a physiological level;

one or more intervention nodes wherein each intervention node comprises an intervention to repair a dysfunction of the user; and

one or more links between the nodes of the expert system, each link from a first node to a second node indicates the probability of the piece of evidence in the first node indicating the piece of evidence in the second node.

50. The system of claim 49, wherein the expert system generates one or more different types of interventions and a probability of efficacy for each health care intervention and wherein all of the health care interventions are ranked according to the probability of efficacy.

51. The system of claim 49, wherein the one or more nodes further comprises one or more composite nodes wherein each composite node comprises an action to be taken based on two or more pieces of health information inputs.

52. The system of claim 49, wherein the probability associated with each link further comprises a true positive probability and a false positive probability.

53. The system of claim 52, wherein each node further comprises an apriori probability of being true.

54. The system of claim 53, wherein each node generates a probability of the node being true wherein the probability is based on the true positive and false positive probabilities of the link to the node and the apriori probability associated with the node.

55. The system of claim 49, wherein each node generates a probability of the dysfunction contained in the node wherein the probability is Boolean.

56. The system of claim 49, wherein the nodes and links of the expert system are stored in the database as one or more database tables.

57. The system of claim 56, wherein the database further comprises one or more tables that store health care related literature.

58. The system of claim 49, wherein each node generates one or more smart questions based on the information provided from the user, the smart questions generated in order to gather more information about the user.

59. The system of claim 58, wherein the expert system prioritizes the smart questions generated by the nodes of the expert system based on the probability that the question will lead to further information about the user.

60. The system of claim 54, wherein each node probability further comprises

Pd|f=Pf*Pa*tp/(Pa*tp+(1−

Pa)*fp)

where Pf is the apriori probability of the “

finding”

or input node, tp and fp are the true and false positive fractions, Pa is the apriori probability and Pd|f is the posterior probability given the “

finding”

for the current node.

61. The system of claim 49, wherein the nodes further comprise an interactions node which identifies interactions between recommendations wherein the interactions node identifies drug therapy side-effects and identifies nutrients to counteract the drug therapy side-effects.

62. A computer-implemented personalized health care expert system, comprising:

one or more input nodes wherein each input node comprises a piece of health information input into the expert system;

one or more damaging factor nodes wherein each damaging factor node comprises a cause of dysfunction;

one or more concept nodes wherein each concept node comprises an abstract dysfunction of a user;

one or more endnode nodes wherein each endnode node comprises a dysfunction of a user at a physiological level;

one or more intervention nodes wherein each intervention node comprises an intervention to repair a dysfunction of the user;

one or more links between the nodes of the expert system, each link from a first node to a second node indicates the probability of the piece of evidence in the first node indicating the piece of evidence in the second node; and

wherein the nodes and links of the expert system are stored in a relational database with each node and each link being stored in a table of the relational database.