Disease predictions

• US 20070015971A1
• Filed: 05/14/2003
• Published: 01/18/2007
• Est. Priority Date: 05/14/2003
• Status: Abandoned Application

• Current Assignee: Clinigene International Private Limited, Strand Genomics Private Limited
• Original Assignee: Clinigene International Private Limited, Strand Genomics Private Limited
• Inventors: Gowda, Halasingana Halli, Narasimha, Mandyam, Atignal, Shankara, Chandru, Vijay, Kalyanasundaram, Subramaniam, Rajput, Anuradha
• Application Number:
  US10/555,225
• Publication Number:
  US 20070015971A1
• Time in Patent Office: Days
• US Class Current: 600/300
• 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: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Abstract

A support vector machine (110) is used to predict who, among a population of patients with diabetes mellitus, will develop proteinuria which is in indicator of diabetic nephropathy. The support vector machine (110) is trained using test results of the patients from blood biochemistry and haemotology tests. The training and testing of the support vector machine (110) used data in which the entire patient population did not exhibit signs of proteinuria at a predetermined time period and three months later, and some of the patient population had proteinuria six months from the predetermined time period. The support vector machine (110) is used to predict who, among patients with diabetes mellitus using lest results from a predetermined time period and three months later, will develop proteinuria at six months from the predetermined time period. The input data to the support vector machine (110) included different parameters of test results at a predetermined time and three months later.

Claims

1. A method of disease prediction comprising:

using a machine learning tool to predict whether a member from a first class will belong to a second class after a predetermined amount of time, wherein members of said first class and said second class have a particular disease, and members of said first class do not have a particular complication after said predetermined amount of time and members of said second class do have said particular complication after said predetermined amount of time.

2. The method of claim 1, wherein said machine learning tool is used to predict who, among patients with diabetes mellitus, will develop proteinuria.

3. The method of claim 1, further comprising:

training said machine learning tool to minimize false positives wherein each of said false positives is defined as a number of patients incorrectly identified as developing proteinuria.

4. The method of claim 3, further comprising:

training said machine learning tool to maximize true positives wherein each of said true positives is defined as a number of patients correctly identified as developing proteinuria.

5. The method of claim 2, wherein said machine learning tool is a support vector machine, members of said first class have diabetes mellitus and do not have proteinuria after said predetermined amount of time, and members of said second class have diabetes mellitus and do have proteinuria after said predetermined amount of time.

6. The method of claim 5, further comprising:

predicting whether a member of said first class, given at least one input parameter at a first time period and three months later, will be a member of said second class six months from said first time period.

7. The method of claim 6, wherein at least one input parameter includes a value obtained using haemotology and blood biochemistry tests.

8. The method of claim 6, wherein said at least one input parameter is selected from the group consisting of:

albumin, alkaline phosphates, SGOT, SGPT, calcium, cholesterol, chloride, creatinine kinase, creatinine, bicarbonate, iron, gamma GT, glucose, HDL cholesterol, potassium, lactate dehydrogenase, LDL, magnesium, sodium, phosphorus, total bilirubin, total protein, triglycerides, UIBC, urea, uric acid, glycosylated haemoglobin, white blood cells, differential counts, neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood corpuscles, hemoglobin, hematocrit, mean cell volume, mean cell hemoglobin, mean cell haemoglobin concentration, platelet count, erythrocyte sedimentation rate, reticulocyte count, peripheral smear, blood grouping, pH, specific gravity, glucose, protein, ketones, urobilinogen, ilirubin, nitrites, leukocytes, erythrocytes, epithelial cells, casts, and crystals.

9. The method of claim 8, wherein said at least one input parameter includes at least one difference parameter defined as a difference between a first value at said first time period and a second value three months later.

10. The method of claim 9, wherein said input parameters include potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

11. The method of claim 9, wherein said input parameters are six difference parameters, each of said six difference parameters representing a difference between test values of one of six tests at a first time period and three months later, said six tests being potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

12. The method of claim 5, wherein said support vector machine uses a Gaussian kernel function in defining a non-linear separating surface to separate members of said first class and said second class.

13. The method of claim 1, wherein said machine learning tool is used to predict who, among patients with diabetes mellitus, will develop diabetic nephropathy.

14. The method of claim 13, wherein at least one indicator is used to detect diabetic nephropathy, and the at least one indicator includes proteinuria.

15. The method of claim 6, further comprising:

partitioning an input data set into six partitions, each of said six partitions being approximately a same size and including an equal number of randomly selected members who belong to said second class at six months from said first time period and who are not in said second class at said first time period and three months later.

16. The method of claim 15, further comprising:

training said support vector machine with five of said six partitions; and

testing said support vector machine with said sixth partition.

17. A computer program product used for disease prediction comprising:

a machine learning tool that predicts whether a member from a first class will belong to a second class after a predetermined amount of time, wherein members of said first class and said second class have a particular disease, and members of said first class do not have a particular complication after said predetermined amount of time and members of said second class do have said particular complication after said predetermined amount of time.

18. The computer program product of claim 17, wherein said machine learning tool is used to predict who, among patients with diabetes mellitus, will develop proteinuria.

19. The computer program product of claim 17, further comprising:

machine executable code that trains said machine learning tool to minimize false positives wherein each of said false positives is defined as a number of patients incorrectly identified as developing proteinuria.

20. The computer program product of claim 19, further comprising:

machine executable code that trains said machine learning tool to maximize true positives wherein each of said true positives is defined as a number of patients correctly identified as developing proteinuria.

21. The computer program product of claim 18, wherein said machine learning tool is a support vector machine, members of said first class have diabetes mellitus and do not have proteinuria, and members of said second class have diabetes mellitus and do have proteinuria.

22. The computer program product of claim 21, further comprising:

machine executable code that predicts whether a member of said first class, given at least one input parameter at a first time period and three months later, will be a member of said second class six months from said first time period.

23. The computer program product of claim 22, wherein at least one input parameter includes a value obtained using haemotology and blood biochemistry tests.

24. The computer program product of claim 22, wherein said at least one input parameter is selected from the group consisting of:

albumin, alkaline phosphates, SGOT, SGPT, calcium, cholesterol, chloride, creatinine kinase, creatinine, bicarbonate, iron, gamma GT, glucose, HDL cholesterol, potassium, lactate dehydrogenase, LDL, magnesium, sodium, phosphorus, total bilirubin, total protein, triglycerides, UIBC, urea, uric acid, glycosylated haemoglobin, white blood cells, differential counts, neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood corpuscles, hemoglobin, hematocrit, mean cell volume, mean cell hemoglobin, mean cell haemoglobin concentration, platelet count, erythrocyte sedimentation rate, reticulocyte count, peripheral smear, blood grouping, pH, specific gravity, glucose, protein, ketones, urobilinogen, ilirubin, nitrites, leukocytes, erythrocytes, epithelial cells, casts, and crystals.

25. The computer program product of claim 24, wherein said at least one input parameter includes at least one difference parameter defined as a difference between a first value at said first time period and a second value three months later.

26. The computer program product of claim 25, wherein said input parameters include potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

27. The computer program product of claim 25, wherein said input parameters are six difference parameters, each of said six difference parameters representing a difference between test values of one of six tests at a first time period and three months later, said six tests being potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

28. The computer program product of claim 21, wherein said support vector machine uses a Gaussian kernel function in defining a non-linear separating surface to separate members of said first class and said second class.

29. The computer program product of claim 17, wherein said machine learning tool is used to predict who, among patients with diabetes mellitus, will develop diabetic nephropathy.

30. The computer program product of claim 29, wherein at least one indicator is used to detect diabetic nephropathy, and the at least one indicator includes proteinuria.

31. The computer program product of claim 22, further comprising:

machine executable code that partitions an input data set into six partitions, each of said six partitions being approximately a same size and including an equal number of randomly selected members who belong to said second class at six months from said first time period and who are not in said second class at said first time period and three months later.

32. The computer program product of claim 31, further comprising:

machine executable code that trains said support vector machine with five of said six partitions; and

machine executable code that tests said support vector machine with said sixth partition.

33. A method of producing a support vector machine used in disease prediction comprising:

partitioning an input data set into a training data set and a testing data set, said input data set including members belonging to a first class and members belonging to a second class, wherein members of said first class and said second class have a particular disease, and members of said first class do not have a particular complication at a first time period and three and six months after said first time period and members of said second class have said particular complication at six months from said first time period, but not at said first time period and three months later.

34. The method of claim 33, further comprising:

training said machine support vector machine to minimize false positives wherein each of said false positives is defined as a number of patients incorrectly identified as developing proteinuria.

35. The method of claim 34, further comprising:

training said support vector machine to maximize true positives wherein each of said true positives is defined as a number of patients correctly identified as developing proteinuria.

36. The method of claim 35, wherein members of said first class have diabetes mellitus and do not have proteinuria, and members of said second class have diabetes mellitus and do have proteinuria at six months from said first time period.

37. The method of claim 36, wherein said input data set includes, for each member, at least one input parameter that is a value obtained from haemotology and blood biochemistry tests.

38. The method of claim 37, wherein said at least one input parameter is selected from the group consisting of:

albumin, alkaline phosphates, SGOT, SGPT, calcium, cholesterol, chloride, creatinine kinase, creatinine, bicarbonate, iron, gamma GT, glucose, HDL cholesterol, potassium, lactate dehydrogenase, LDL, magnesium, sodium, phosphorus, total bilirubin, total protein, triglycerides, UIBC, urea, uric acid, glycosylated haemoglobin, white blood cells, differential counts, neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood corpuscles, hemoglobin, hematocrit, mean cell volume, mean cell hemoglobin, mean cell haemoglobin concentration, platelet count, erythrocyte sedimentation rate, reticulocyte count, peripheral smear, blood grouping, pH, specific gravity, glucose, protein, ketones, urobilinogen, ilirubin, nitrites, leukocytes, erythrocytes, epithelial cells, casts, and crystals.

39. The method of claim 38, wherein said at least one input parameter includes at least one difference parameter defined as a difference between a first value at said first time period and a second value three months later.

40. The method of claim 39, wherein said input parameters include potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

41. The method of claim 39, wherein said input parameters are six difference parameters, each of said six difference parameters representing a difference between test values of one of six tests at a first time period and three months later, said six tests being potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

42. The method of claim 33, wherein said support vector machine uses a Gaussian kernel function in defining a non-linear separating surface to separate members of said first class and said second class.

43. The method of claim 33, further comprising:

partitioning said input data set into six partitions, each of said six partitions being approximately a same size and including an equal number of randomly selected members who belong to said second class at six months from a first time period and who are not in said second class at said first time period and three months later;

training said support vector machine with five of said six partitions; and

testing said support vector machine with said sixth partition.

44. A computer program product that produces a support vector machine used in disease prediction comprising:

machine executable code that partitions an input data set into a training data set and a testing data set, said input data set including members belonging to a first class and members belonging to a second class, wherein members of said first class and said second class have a particular disease, and members of said first class do not have a particular complication at a first time period and three and six months after said first time period and members of said second class have said particular complication at six months from said first time period, but not at said first time period and three months later.

45. The computer program product of claim 44, further comprising:

machine executable code that trains said machine support vector machine to minimize false positives wherein each of said false positives is defined as a number of patients incorrectly identified as developing proteinuria.

46. The computer program product of claim 45, further comprising:

machine executable code that trains said support vector machine to maximize true positives wherein each of said true positives is defined as a number of patients correctly identified as developing proteinuria.

47. The computer program product of claim 46, wherein members of said first class have diabetes mellitus and do not have proteinuria, and members of said second class have diabetes mellitus and do have proteinuria at six months from said first time period.

48. The computer program product of claim 47, wherein said input data set includes, for each member, at least one input parameter that is a value obtained from haemotology and blood biochemistry tests.

49. The computer program product of claim 48, wherein said at least one input parameter is selected from the group consisting of:

albumin, alkaline phosphates, SGOT, SGPT, calcium, cholesterol, chloride, creatinine kinase, creatinine, bicarbonate, iron, gamma GT, glucose, HDL cholesterol, potassium, lactate dehydrogenase, LDL, magnesium, sodium, phosphorus, total bilirubin, total protein, triglycerides, UIBC, urea, uric acid, glycosylated haemoglobin, white blood cells, differential counts, neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood corpuscles, hemoglobin, hematocrit, mean cell volume, mean cell hemoglobin, mean cell haemoglobin concentration, platelet count, erythrocyte sedimentation rate, reticulocyte count, peripheral smear, blood grouping, pH, specific gravity, glucose, protein, ketones, urobilinogen, ilirubin, nitrites, leukocytes, erythrocytes, epithelial cells, casts, and crystals.

50. The computer program product of claim 49, wherein said at least one input parameter includes at least one difference parameter defined as a difference between a first value at said first time period and a second value three months later.

51. The computer program product of claim 50, wherein said input parameters include potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

52. The computer program product of claim 50, wherein said input parameters are six difference parameters, each of said six difference parameters representing a difference between test values of one of six tests at a first time period and three months later, said six tests being potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

53. The computer program product of claim 44, wherein said support vector machine uses a Gaussian kernel function in defining a non-linear separating surface to separate members of said first class and said second class.

54. The computer program product of claim 44, further comprising:

machine executable code that partitions said input data set into six partitions, each of said six partitions being approximately a same size and including an equal number of randomly selected members who belong to said second class at six months from a first time period and who are not in said second class at said first time period and three months later;

machine executable code that trains said support vector machine with five of said six partitions; and

machine executable code that tests said support vector machine with said sixth partition.

55. A method of disease prediction comprising:

using a support vector machine to predict whether a member from a first class will belong to a second class after a predetermined amount of time, wherein members of said first class and said second class have diabetes mellitus, and members of said first class do not have proteinuria after said predetermined amount of time and members of said second class do have proteinuria after said predetermined amount of time, wherein input data of a patient used to predict whether the patient will belong to said first class or said second class includes input parameters based on test results including potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

56. The method of claim 55, further comprising:

training said support vector machine to minimize false positives wherein each of said false positives is defined as a number of patients incorrectly identified as developing proteinuria.

57. The method of claim 56, further comprising:

training said support vector machine to maximize true positives wherein each of said true positives is defined as a number of patients correctly identified as developing proteinuria.

58. The method of claim 55, wherein said input data is based on test results of said patient at a first time period and three months later to predict whether the patient will develop proteinuria at six months from said first time period.

59. The method of claim 55, wherein said input data is based on test results of said patient at a first time period and three months later to predict whether the patient will develop diabetic nephropathy at six months from said first time period.

60. The method of claim 59, wherein said input parameters include at least one difference parameter defined as a difference between a first value of a test result at said first time period and a second value of said test result three months later.

61. The method of claim 60, wherein said input parameters are six difference parameters, each of said six difference parameters representing a difference between test values of one of six tests at a first time period and three months later, said six tests being potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

62. The method of claim 61, wherein said support vector machine uses a Gaussian kernel function in defining a non-linear separating surface to separate members of said first class and said second class.

63. The method of claim 57, further comprising:

partitioning said input data set into six partitions, each of said six partitions being approximately a same size and including an equal number of randomly selected members who belong to said second class at six months from said first time period and who are not in said second class at said first time period and three months later.

64. A computer program product used for disease prediction comprising:

a support vector machine that predicts whether a member from a first class will belong to a second class after a predetermined amount of time, wherein members of said first class and said second class have diabetes mellitus, and members of said first class do not have proteinuria after said predetermined amount of time and members of said second class do have proteinuria after said predetermined amount of time, wherein input data of a patient used to predict whether the patient will belong to said first class or said second class includes input parameters based on test results including potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

65. The computer program product of claim 64, further comprising:

machine executable code that trains said support vector machine to minimize false positives wherein each of said false positives is defined as a number of patients incorrectly identified as developing proteinuria.

66. The computer program product of claim 65, further comprising:

machine executable code that trains said support vector machine to maximize true positives wherein each of said true positives is defined as a number of patients correctly identified as developing proteinuria.

67. The computer program product of claim 64, wherein said input data is based on test results of said patient at a first time period and three months later to predict whether the patient will develop proteinuria at six months from said first time period.

68. The computer program product of claim 64, wherein said input data is based on test results of said patient at a first time period and three months later to predict whether the patient will develop diabetic nephropathy at six months from said first time period.

69. The computer program product of claim 68, wherein said input parameters include at least one difference parameter defined as a difference between a first value of a test result at said first time period and a second value of said test result three months later.

70. The computer program product of claim 69, wherein said input parameters are six difference parameters, each of said six difference parameters representing a difference between test values of one of six tests at a first time period and three months later, said six tests being potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

71. The computer program product method of claim 70, wherein said support vector machine uses a Gaussian kernel function in defining a non-linear separating surface to separate members of said first class and said second class.

72. The computer program product of claim 66, further comprising:

machine executable code that partitions said input data set into six partitions, each of said six partitions being approximately a same size and including an equal number of randomly selected members who belong to said second class at six months from said first time period and who are not in said second class at said first time period and three months later.

73. The computer program product of claim 72, further comprising:

machine executable code that trains said support vector machine with five of said six partitions; and

machine executable code that tests said support vector machine with said sixth partition.

74. A computer-implemented method for disease prediction comprising:

predicting whether a member from a first class will belong to a second class after a predetermined amount of time, wherein members of said first class and said second class have diabetes mellitus, and members of said first class do not have proteinuria after said predetermined amount of time and members of said second class do have proteinuria after said predetermined amount of time, wherein said input data of a patient used to predict whether the patient will belong to said first class or said second class includes input parameters based on test results including potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

75. The method of claim 74, wherein said input data includes at least one difference parameter that is a difference of a test result at a first time period and three months later.

76. The method of claim 75, further comprising:

using said input data of a patient to predict whether the patient will develop proteinuria after 6 months from said first time period.

77. A computer program product for disease prediction comprising:

machine executable code that predicts whether a member from a first class will belong to a second class after a predetermined amount of time, wherein members of said first class and said second class have diabetes mellitus, and members of said first class do not have proteinuria after said predetermined amount of time and members of said second class do have proteinuria after said predetermined amount of time, wherein said input data of a patient used to predict whether the patient will belong to said first class or said second class includes input parameters based on test results including potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

78. The computer program product of claim 77, wherein said input data includes at least one difference parameter that is a difference of a test result at a first time period and three months later.

79. The computer program product of claim 77, further comprising:

machine executable code that uses said input data of a patient to predict whether the patient will develop proteinuria after 6 months from said first time period.

80. A computer-implemented method for producing a machine-learning tool used in disease prediction, the method comprising:

training said machine-learning tool using training data to predict whether a member from a first class will belong to a second class after a predetermined amount of time, wherein members of said first class and said second class have diabetes mellitus, and members of said first class do not have proteinuria after said predetermined amount of time and members of said second class do have proteinuria after said predetermined amount of time, wherein said training data includes, for each patient, input parameters based on test results including potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

81. The method of claim 80, wherein said training data includes at least one difference parameter that is a difference of a test result at a first time period and three months later.

82. The method of claim 80, further comprising:

using input data of a patient to predict whether the patient will develop proteinuria after 6 months from said first time period.

83. A computer program product for producing a machine-learning tool used in

disease prediction, the computer program product comprising:

machine executable code that trains said machine-learning tool using training data to predict whether a member from a first class will belong to a second class after a predetermined amount of time, wherein members of said first class and said second class have diabetes mellitus, and members of said first class do not have proteinuria after said predetermined amount of time and members of said second class do have proteinuria after said predetermined amount of time, wherein said training data includes, for each patient, input parameters based on test results including potassium, SGPT, glycosylated haemoglobin, cholesterol, chloride and LDL.

84. The computer program product of claim 83, wherein said training data includes at least one difference parameter that is a difference of a test result at a first time period and three months later.

85. The method of claim 83, further comprising:

machine executable code that uses input data of a patient to predict whether the patient will develop proteinuria after 6 months from said first time period.