Methods and systems for generating diagnostic algorithms based on questionnaires

• US 20060122465A1
• Filed: 06/12/2003
• Published: 06/08/2006
• Est. Priority Date: 06/13/2002
• Status: Abandoned Application

• Current Assignee: L'Oreal SA, Renesas Technology Corporation (Renesas Electronics Corporation)
• Original Assignee: L'Oreal SA
• Inventors: De LaCharriere, Olivier, Jourdain, Roland, Bastien, Philippe
• Application Number:
  US10/517,391
• Publication Number:
  US 20060122465A1
• Time in Patent Office: Days
• US Class Current: 600/300
• 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 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

Abstract

Methods and systems of the present invention generally relate to questionnaires and diagnostic algorithms. Features and principles of the present invention may include a method of generating a diagnostic algorithm. The method may involve receiving information reflecting a plurality of individuals'"'"' answers to questions, performing an analysis on the received information to generate a synthetic variable, and generating a diagnostic algorithm from at least the synthetic variable using a tree segmentation technique.

Claims

1. A diagnostic method comprising:

providing a first question;

receiving first information reflecting an answer to the first question;

selecting a second question according to the first information and according to a diagnostic algorithm generated using at least one of a multivariate analysis and a tree segmentation technique;

providing the second question;

receiving second information reflecting an answer to the second question; and

determining a diagnosis according to the diagnostic algorithm.

2. The method according to claim 1, wherein the multivariate analysis includes at least one of a principal component analysis, a factorial analysis, PLS path modeling, and structural equation modeling.

3. The method according to claim 1, wherein the tree segmentation technique includes a classification and regression tree method.

4. The method according to claim 3, wherein the classification and regression tree method is at least one of a CART method, a CHAID method, and a QUEST method.

5. The method according to claim 1, wherein the diagnostic algorithm is a diagnostic algorithm generated using at least both the multivariate analysis and the tree segmentation technique.

6. The method according to claim 5, wherein the multivariate analysis includes a principal component analysis and wherein the tree segmentation technique includes a classification and regression tree method.

7. The method according to claim 1, wherein the first question is a most discriminating question according to the tree segmentation technique.

8. The method according to claim 7, wherein the second question is, according to the tree segmentation technique and the answer to the first question, a second most discriminating question.

9. The method according to claim 1, wherein at least one of the first information and the second information is received from a remote location over a network.

10. The method according to claim 9, wherein the network is at least one of an internet, an intranet, a wireless network, and a wired network.

11. The method according to claim 1, wherein at least one of the first information and the second information is received from a portable electronic device.

12. The method according to claim 11, wherein the portable electronic device is at least one of a handheld personal computer, a telephone, a mobile phone, a television set, and a personal organizer.

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

selecting at least one subsequent question according to the diagnostic algorithm;

providing the at least one subsequent question; and

receiving subsequent information reflecting at least one answer to the at least one subsequent question.

14. The method according to claim 13, wherein the selecting and providing of at least one subsequent question continues until the determining of the diagnosis.

15. The method according to claim 1, wherein the diagnostic algorithm is a diagnostic algorithm generated by at least:

receiving, from a plurality of individuals, initial information reflecting answers to initial questions;

performing the multivariate analysis on the initial information received from the plurality of individuals to generate at least one synthetic variable; and

generating the diagnostic algorithm from at least the synthetic variable using the tree segmentation technique.

16. The method according to claim 1, wherein at least one of providing the first question and providing the second question comprises presenting, respectively, the first question or the second question at least one of over a network, at a kiosk, in a waiting room, at home, and at a point-of-sale.

17. The method according to claim 16, wherein the network includes at least one of an internet, an intranet, a wireless network, and a wired network.

18. The method according to claim 16, wherein the kiosk includes a display to present at least one of the first question and the second question.

19. The method according to claim 16, wherein the point-of-sale is at least one of a product-selling store, a service-providing location, and a website.

20. The method according to claim 1, wherein the diagnosis is at least one of a dermatological diagnosis, a beauty diagnosis, and a cosmetic diagnosis.

21. The method according to claim 1, wherein the diagnosis relates to at least one of a skin characteristic and a keratin characteristic.

22. The method according to claim 21, wherein the keratin characteristic relates to at least one of a hair, a nail, an eyelash, and an eyebrow.

23. The method according to claim 1, wherein the diagnosis relates to at least one of a skin condition and a keratin condition.

24. The method according to claim 23, wherein the keratin condition relates to at least one of a hair, a nail, an eyelash, and an eyebrow.

25. The method according to claim 23, wherein the skin condition includes at least one of greasy skin, dry skin, aging skin, wrinkled skin, marked skin, flask skin, squeamish skin, sensitive skin, skin phototype, a pigmented spot of skin, a problem with an eyelid, skin topography, a sensitive lip, a wrinkle around a lip, acne, and eczema.

26. The method according to claim 23, wherein the keratin condition includes at least one of hair loss, hair shine, hair thickness, hair oiliness, hair health, hair graying, and hair color.

27. The method according to claim 1, further comprising selecting at least one product according to, at least in part, the diagnosis.

28. The method according to claim 27, further comprising offering the product for sale.

29. The method according to claim 28, wherein the product includes at least one of a good and a service.

30. The method according to claim 28, wherein the product is a beauty product.

31. The method according to claim 1, further comprising providing at least one of advice and a recommendation according to, at least in part, the diagnosis.

32. The method according to claim 1, further comprising selecting at least one subject individual according to, at least in part, the diagnosis.

33. The method according to claim 32, wherein the subject individual exhibits a desired characteristic.

34. The method according to claim 33, wherein the desired characteristic is sensitive skin.

35. The method according to claim 34, further comprising evaluating a product on the subject individual.

86. A system, comprising:

a data processor; and

a storage medium functionally coupled to the data processor, wherein the storage medium contains instructions to be executed by the data processor for performing the method according to claim 1.

89. A computer program product, comprising a computer-readable medium, wherein the computer-readable medium contains instructions for executing the method according to claim 1.

36. A diagnostic method, comprising:

receiving a first question;

sending first information reflecting an answer to the first question;

receiving a second question, wherein the second question is a question selected according to the first information and according to a diagnostic algorithm generated using at least one of a multivariate analysis and a tree segmentation technique; and

sending second information reflecting an answer to the second question,

wherein a diagnosis is determined according to the diagnostic algorithm.

37. The method according to claim 36, wherein the multivariate analysis includes at least one of a principal component analysis, a factorial analysis, PLS path modeling, and structural equation modeling.

38. The method according to claim 36, wherein the tree segmentation technique includes a classification and regression tree method.

39. The method according to claim 38, wherein the classification and regression tree method is at least one of a CART method, a CHAID method, and a QUEST method.

40. The method according to claim 36, wherein the diagnostic algorithm is a diagnostic algorithm generated using at least both the multivariate analysis and the tree segmentation technique.

41. The method according to claim 40, wherein the multivariate analysis includes a principal component analysis and wherein the tree segmentation technique includes a classification and regression tree method.

42. The method according to claim 36, wherein the first question is a most discriminating question according to the tree segmentation technique.

43. The method according to claim 42, wherein the second question is, according to the tree segmentation technique and the answer to the first question, a second most discriminating question.

44. The method according to claim 36, wherein at least one of the first information and the second information is sent to a remote location over a network.

45. The method according to claim 44, wherein the network is at least one of an internet, an intranet, a wireless network, and a wired network.

46. The method according to claim 36, wherein at least one of the first information and the second information is sent by a portable electronic device.

47. The method according to claim 46, wherein the portable electronic device is at least one of a handheld personal computer, a telephone, a mobile phone, a television set, and a personal organizer.

48. The method according to claim 36, further comprising:

receiving at least one subsequent question selected according to the diagnostic algorithm; and

sending subsequent information reflecting at least one answer to the at least one subsequent question.

49. The method according to claim 48, wherein the receiving of at least one subsequent question continues until the diagnosis is determined.

50. The method according to claim 36, wherein the diagnostic algorithm is a diagnostic algorithm generated by at least:

receiving, from a plurality of individuals, initial information reflecting answers to initial questions;

performing the multivariate analysis on the initial information received from the plurality of individuals to generate at least one synthetic variable; and

generating the diagnostic algorithm from at least the synthetic variable using the tree segmentation technique.

51. The method according to claim 36, wherein at least one of the first question and the second question is received at least one of over a network, at a kiosk, in a waiting room, at home, and at a point-of-sale.

52. The method according to claim 51, wherein the network includes at least one of an internet, an intranet, a wireless network, and a wired network.

53. The method according to claim 51, wherein the kiosk includes a display to present at least one of the first question and the second question.

54. The method according to claim 51, wherein the point-of-sale is at least one of a product-selling store, a service-providing location, and a website.

55. The method according to claim 36, wherein the diagnosis is at least one of a dermatological diagnosis, a beauty diagnosis, and a cosmetic diagnosis.

56. The method according to claim 36, wherein the diagnosis relates to at least one of a skin characteristic and a keratin characteristic.

57. The method according to claim 56, wherein the keratin characteristic relates to at least one of a hair, a nail, an eyelash, and an eyebrow.

58. The method according to claim 36, wherein the diagnosis relates to at least one of a skin condition and a keratin condition.

59. The method according to claim 58, wherein the keratin condition relates to at least one of a hair, a nail, an eyelash, and an eyebrow.

60. The method according to claim 58, wherein the skin condition includes at least one of greasy skin, dry skin, aging skin, wrinkled skin, marked skin, flask skin, squeamish skin, sensitive skin, skin phototype, a pigmented spot of skin, a problem with an eyelid, skin topography, a sensitive lip, a wrinkle around a lip, acne, and eczema.

61. The method according to claim 58, wherein the keratin condition includes at least one of hair loss, hair shine, hair thickness, hair oiliness, hair health, hair graying, and hair color.

62. The method according to claim 36, further comprising selecting at least one product according to, at least in part, the diagnosis.

63. The method according to claim 62, further comprising buying the product.

64. The method according to claim 63, wherein the product includes at least one of a good and a service.

65. The method according to claim 63, wherein the product is a beauty product.

66. The method according to claim 36, further comprising receiving at least one of advice and a recommendation provided according to, at least in part, the diagnosis.

67. The method according to claim 36, further comprising receiving third information reflecting the diagnosis.

87. A system, comprising:

a data processor; and

a storage medium functionally coupled to the data processor, wherein the storage medium contains instructions to be executed by the data processor for performing the method according to claim 36.

90. A computer program product, comprising a computer-readable medium, wherein the computer-readable medium contains instructions for executing the method according to claim 36.

68. A method of generating a diagnostic algorithm, comprising:

receiving information reflecting a plurality of individuals'"'"' answers to questions;

performing an analysis on the received information to generate a synthetic variable; and

generating a diagnostic algorithm from at least the synthetic variable using a tree segmentation technique.

69. The method according to claim 68, wherein the method further comprises presenting, to a plurality of individuals, a questionnaire including the questions, and wherein receiving the information comprises collecting answers to questions of the questionnaire.

70. The method according to claim 69, wherein the questionnaire and the answers to the questions of the questionnaire are respectively presented and collected over a network.

71. The method according to claim 70, wherein the network is at least one of an internet, intranet, wireless network, and wired network.

72. The method according to claim 68, wherein the analysis is a multivariate analysis.

73. The method according to claim 72, wherein the multivariate analysis includes at least one of a principal component analysis, a factorial analysis, PLS path modeling, and structural equation modeling.

74. The method according to claim 73, wherein the principal component analysis is hierarchical.

75. The method according to claim 68, wherein the tree segmentation technique includes a classification and regression tree method.

76. The method according to claim 75, wherein the classification and regression tree method is at least one of a CART method, a CHAID method, and a QUEST method.

77. The method according to claim 68, wherein performing the analysis on the received information to generate a synthetic variable comprises:

selecting, from the received information, at least two groups of information reflecting answers that relate to differing sub-topics, respectively;

performing an analysis on each selected group of information to generate respective sub-topic variables; and

performing an analysis on the sub-topic variables to generate the synthetic variable.

78. The method according to claim 77, wherein the sub-topic variables correspond to a first factorial axis of a principal component analysis.

79. The method according to claim 77, wherein the sub-topics include at least one of atopy, sensitivity, cosmetic reactivity, environmental reactivity, and vascular reactivity.

80. The method according to claim 68, wherein the synthetic variable corresponds to a first factorial axis of a principal component analysis.

81. The method according to claim 68, wherein the diagnostic algorithm diagnoses at least an overall sensitivity of a subject individual.

82. The method according to claim 68, further comprising transforming the synthetic variable with a linear transformation.

83. The method according to claim 68, wherein generating the diagnostic algorithm using the tree segmentation technique includes:

selecting most discriminating questions; and

generating a limited set of the most discriminating questions that can provide a diagnosis by progressing through a tree.

84. The method according to claim 83, wherein the diagnostic is associated with a terminating branch in the tree.

85. The method according to claim 68, wherein the diagnostic algorithm provides at least one of a dermatological diagnosis, a beauty diagnosis, and a cosmetic diagnosis.

88. A system, comprising:

a data processor; and

a storage medium functionally coupled to the data processor, wherein the storage medium contains instructions to be executed by the data processor for performing the method according to claim 68.

91. A computer program product, comprising a computer-readable medium, wherein the computer-readable medium contains instructions for executing the method according to claim 68.