Medical Data Mining

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Medical Data Mining

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

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