This document discusses how medical data mining of Danish healthcare registries can be used to link diseases, drugs, and adverse reactions. It describes how the Danish registries contain structured and unstructured data from hospitals and central registries dating back to 1968. Text mining of unstructured clinical notes allows identification of diseases, drugs, and possible adverse drug reactions. Analysis of this data through methods like comorbidity, diagnosis trajectories, and temporal correlations can provide new insights into disease relationships and help discover new adverse drug reactions and their frequencies.

1. Medical data mining
Linking diseases, drugs, and adverse
reactions
Lars Juhl Jensen

2. unstructured data

4. structured data

5. Jensen et al., Nature Reviews Genetics, 2012

6. individual hospitals

7. central registries

9. opt-out

10. opt-in

11. Danish registries

12. civil registration system

13. CPR number

14. established in 1968

15. Jensen et al., Nature Reviews Genetics, 2012

16. national discharge registry

17. 14 years

18. 6.2 million patients

19. 45 million admissions

20. 68 million records

21. 119 million diagnosis

22. ICD-10

23. Jensen et al., Nature Reviews Genetics, 2012

24. reimbursement

25. not research

26. diagnosis trajectories

27. naïve approach

28. comorbidity

29. Jensen et al., Nature Reviews Genetics, 2012

30. confounding factors

31. “known knowns”

32. gender

33. age

34. type of hospital encounter

35. Male
Emergency room
Out-patient
In-patient
Female
Jensen et al., submitted, 2013

36. “known unknowns”

37. smoking

38. diet

39. “unknown unknowns”

40. reporting biases

41. disease clustering

43. temporal correlation

44. Jensen et al., submitted, 2013

45. diagnosis trajectories

46. Jensen et al., submitted, 2013

47. epilepsy

48. Jensen et al., submitted, 2013

49. gout

50. Jensen et al., submitted, 2013

51. electronic health records

52. structured data

53. Jensen et al., Nature Reviews Genetics, 2012

54. unstructured data

56. free text

57. Danish

58. busy doctors

59. psychiatric patients

60. delusions

61. text mining

62. named entity recognition

63. custom dictionaries

64. diseases

65. drugs

66. adverse drug events

67. expansion rules

68. orthographic variation

69. typos

70. “negative modifiers”

71. negations

72. family members

73. detailed disease profiles

74. Text mined codes
Assigned codes
4947 3825
32626
Roque et al., PLOS Computational Biology, 2011

75. comorbidity

76. Roque et al., PLOS Computational Biology, 2011

77. patient stratification

78. Roque et al., PLOS Computational Biology, 2011

79. cluster characterization

80. Roque et al., PLOS Computational Biology, 2011

81. adverse drug reactions

82. structured data

83. medication

84. clinical narrative

85. possible ADRs

86. semi-structured data

87. SPC
Summary of Product Characteristics

88. drug indications

89. known ADRs

90. temporal correlation

91. link drugs to ADRs

92. complex filtering

93. Eriksson et al., submitted, 2013

94. new ADRs

95. Drug substance
ADE
Chlordiazepoxide Nystagmus
Simvastatin
Personality
changes
Dipyridamole
Visual impairment
Citalopram
Psychosis
Bendroflumethiazi Apoplexy
de
p-value
4.0e-8
8.4e-8
4.4e-4
8.8e-4
8.5e-3
Eriksson et al., submitted, 2013

96. ADR frequencies

97. Eriksson et al., submitted, 2013

98. heavily medicated

99. Eriksson et al., submitted, 2013

100. ADR dose dependency

101. Eriksson et al., submitted, 2013

102. ADR similarity

103. Eriksson et al., submitted, 2013

104. drug repurposing

105. Campillos, Kuhn et al., Science, 2008

106. Acknowledgments
EHR text mining
Peter Bjødstrup
Jensen
Robert Eriksson
Henriette Schmock
Francisco S. Roque
Anders Juul
Marlene Dalgaard
Massimo Andreatta
Sune Frankild
Eva Roitmann
Thomas Hansen
Karen Søeby
Søren Bredkjær
Thomas Werge
Søren Brunak
Disease trajectories
Anders Bøck Jensen
Tudor Oprea
Pope Moseley
Søren Brunak
Adverse drug reactions
Robert Eriksson
Thomas Werge
Søren Brunak

107. Thank you!