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

2. structured data

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

4. unstructured data

6. central registries

7. individual hospitals

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. not research

25. reimbursement

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., Nature Communications, 2014

36. “known unknowns”

37. smoking

38. diet

39. “unknown unknowns”

40. reporting biases

41. matched controls

42. temporal correlations

43. multiple testing

44. trajectories

45. Jensen et al., Nature Communications, 2014

46. trajectory networks

47. Jensen et al., Nature Communications, 2014

48. key diagnoses

49. Jensen et al., Nature Communications, 2014

50. direct medical implications

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. typos

60. psychiatric patients

61. text mining

62. computer

63. as smart as a dog

64. teach it specific tricks

67. comprehensive dictionary

68. diseases

69. drugs

70. adverse drug reactions

71. expansion rules

72. Clozapine

73. clozapin clossapin klozapin e chlosapi n chlosapin e chlozapin chlozapin e klossapi n closapin e klozapi n klosapin

74. flexible matching

75. compound nouns

76. post-coordination rules

77. failure of kidney

78. kidney failure

79. “black list”

80. three-letter acronyms

81. pharmacovigilance

82. clinical trials

83. spontaneous reports

85. underreporting

86. data mining

87. structured data

88. medication

89. semi-structured data

90. drug indications

91. known ADRs

92. unstructured data

93. adverse drug reactions

94. temporal correlations

95. hand-crafted rules

96. Eriksson et al., Drug Safety, 2014

100. recall known ADRs

101. estimate ADR frequencies

102. Eriksson et al., Drug Safety, 2014

103. discover new ADRs

104. Drug substanceADEp-value ChlordiazepoxideNystagmus4.0e-8 SimvastatinPersonality changes8.4e-8 DipyridamoleVisual impairment4.4e-4 CitalopramPsychosis8.8e-4 BendroflumethiazideApoplexy8.5e-3 Eriksson et al., Drug Safety, 2014

105. Acknowledgments 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