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. Jensen et al., submitted, 2013 FemaleMale In-patient Out-patient Emergency room

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. Roque et al., PLOS Computational Biology, 2011 3262638254947 Assigned codes Text mined codes

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. Eriksson et al., submitted, 2013 Drug substanceADEp-value ChlordiazepoxideNystagmus4.0e-8 SimvastatinPersonality changes8.4e-8 DipyridamoleVisual impairment4.4e-4 CitalopramPsychosis8.8e-4 BendroflumethiazideApoplexy8.5e-3

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. Disease trajectories Anders Bøck Jensen Tudor Oprea Pope Moseley Søren Brunak Adverse drug reactions Robert Eriksson Thomas Werge Søren Brunak 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 Acknowledgments

107. Thank you!