Medical Data Mining

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Lars Juhl Jensen

Medical Data Mining
Lars Juhl Jensen

unstructured data

structured data

Jensen et al., Nature Reviews Genetics, 2012

individual hospitals

central registries

opt-out

opt-in

Danish registries

civil registration system

CPR number

established in 1968

Jensen et al., Nature Reviews Genetics, 2012

national discharge registry

14 years

6.2 million patients

45 million admissions

68 million records

119 million diagnosis

ICD-10

Jensen et al., Nature Reviews Genetics, 2012

reimbursement

not research

diagnosis trajectories

naïve approach

comorbidity

Jensen et al., Nature Reviews Genetics, 2012

confounding factors

“known knowns”

gender

age

type of hospital encounter

Jensen et al., submitted, 2014

“known unknowns”

smoking

diet

“unknown unknowns”

reporting biases

matched controls

temporal correlation

Jensen et al., Nature Communications, 2014

trajectories

Jensen et al., Nature Communications, 2014

trajectory networks

Jensen et al., Nature Communications, 2014

key diagnoses

Jensen et al., Nature Communications, 2014

direct medical implications

electronic health records

structured data

Jensen et al., Nature Reviews Genetics, 2012

unstructured data

free text

Danish

busy doctors

typos

psychiatric patients

delusions

heavily medicated

Eriksson et al., Drug Safety, 2014

text mining

dictionary-based method

diseases

drugs

adverse drug reactions

expansion rules

typos

“negative modifiers”

negations

delusions

detailed disease profiles

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

pharmacovigilance

structured data

medication

semi-structured data

drug indications

known ADRs

unstructured data

adverse drug reactions

temporal correlation

Eriksson et al., Drug Safety, 2014

known ADRs

ADR frequencies

Eriksson et al., Drug Safety, 2014

new ADRs

Drug substance ADE p-value
Chlordiazepoxide Nystagmus 4.0e-8
Simvastatin Personality
changes
8.4e-8
Dipyridamole Visual impairment 4.4e-4
Citalopram Psychosis 8.8e-4
Bendroflumethiazi
de
Apoplexy 8.5e-3
Eriksson et al., Drug Safety, 2014

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

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Medical Data Mining

1. Medical Data Mining Lars Juhl Jensen

2. unstructured data

3.

4. structured data

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

6. individual hospitals

7. central registries

8.

11. Danish registries

12. civil registration system

14. established in 1968

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

16. national discharge registry

18. 6.2 million patients

19. 45 million admissions

20. 68 million records

21. 119 million diagnosis

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”

34. type of hospital encounter

35. Jensen et al., submitted, 2014

36. “known unknowns”

39. “unknown unknowns”

40. reporting biases

41. matched controls

42. temporal correlation

43. Jensen et al., Nature Communications, 2014

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

55.

58. busy doctors

60. psychiatric patients

62. heavily medicated

63. Eriksson et al., Drug Safety, 2014

64. text mining

65. dictionary-based method

68. adverse drug reactions

69. expansion rules

71. “negative modifiers”

74. detailed disease profiles

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

76. pharmacovigilance

77. structured data

79. semi-structured data

80. drug indications

82. unstructured data

83. adverse drug reactions

84. temporal correlation

85. Eriksson et al., Drug Safety, 2014

87. ADR frequencies

88. Eriksson et al., Drug Safety, 2014

90. Drug substance ADE p-value Chlordiazepoxide Nystagmus 4.0e-8 Simvastatin Personality changes 8.4e-8 Dipyridamole Visual impairment 4.4e-4 Citalopram Psychosis 8.8e-4 Bendroflumethiazi de Apoplexy 8.5e-3 Eriksson et al., Drug Safety, 2014

91. 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