Medical Data Mining
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Medical Data Mining

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Medical Data Mining Presentation Transcript

  • 1. Medical Data Mining Lars Juhl Jensen
  • 2. unstructured data
  • 3. structured data
  • 4. Jensen et al., Nature Reviews Genetics, 2012
  • 5. individual hospitals
  • 6. central registries
  • 7. opt-out
  • 8. opt-in
  • 9. Danish registries
  • 10. civil registration system
  • 11. CPR number
  • 12. established in 1968
  • 13. Jensen et al., Nature Reviews Genetics, 2012
  • 14. national discharge registry
  • 15. 14 years
  • 16. 6.2 million patients
  • 17. 45 million admissions
  • 18. 68 million records
  • 19. 119 million diagnosis
  • 20. ICD-10
  • 21. Jensen et al., Nature Reviews Genetics, 2012
  • 22. reimbursement
  • 23. not research
  • 24. diagnosis trajectories
  • 25. naïve approach
  • 26. comorbidity
  • 27. Jensen et al., Nature Reviews Genetics, 2012
  • 28. confounding factors
  • 29. “known knowns”
  • 30. gender
  • 31. age
  • 32. type of hospital encounter
  • 33. Jensen et al., submitted, 2014
  • 34. “known unknowns”
  • 35. smoking
  • 36. diet
  • 37. “unknown unknowns”
  • 38. reporting biases
  • 39. matched controls
  • 40. temporal correlation
  • 41. Jensen et al., Nature Communications, 2014
  • 42. trajectories
  • 43. Jensen et al., Nature Communications, 2014
  • 44. trajectory networks
  • 45. Jensen et al., Nature Communications, 2014
  • 46. key diagnoses
  • 47. Jensen et al., Nature Communications, 2014
  • 48. direct medical implications
  • 49. electronic health records
  • 50. structured data
  • 51. Jensen et al., Nature Reviews Genetics, 2012
  • 52. unstructured data
  • 53. free text
  • 54. Danish
  • 55. busy doctors
  • 56. typos
  • 57. psychiatric patients
  • 58. delusions
  • 59. heavily medicated
  • 60. Eriksson et al., Drug Safety, 2014
  • 61. text mining
  • 62. dictionary-based method
  • 63. diseases
  • 64. drugs
  • 65. adverse drug reactions
  • 66. expansion rules
  • 67. typos
  • 68. “negative modifiers”
  • 69. negations
  • 70. delusions
  • 71. detailed disease profiles
  • 72. Roque et al., PLOS Computational Biology, 2011 3262638254947 Assigned codes Text mined codes
  • 73. pharmacovigilance
  • 74. structured data
  • 75. medication
  • 76. semi-structured data
  • 77. drug indications
  • 78. known ADRs
  • 79. unstructured data
  • 80. adverse drug reactions
  • 81. temporal correlation
  • 82. Eriksson et al., Drug Safety, 2014
  • 83. known ADRs
  • 84. ADR frequencies
  • 85. Eriksson et al., Drug Safety, 2014
  • 86. new ADRs
  • 87. 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
  • 88. 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