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, 2013 Female Male In-patientOut-patientEmergencyroom
  34. 34. “known unknowns”
  35. 35. smoking
  36. 36. diet
  37. 37. “unknown unknowns”
  38. 38. reporting biases
  39. 39. disease clustering
  40. 40. temporal correlation
  41. 41. Jensen et al., submitted, 2013
  42. 42. diagnosis trajectories
  43. 43. Jensen et al., submitted, 2013
  44. 44. epilepsy
  45. 45. Jensen et al., submitted, 2013
  46. 46. gout
  47. 47. Jensen et al., submitted, 2013
  48. 48. electronic health records
  49. 49. structured data
  50. 50. Jensen et al., Nature Reviews Genetics, 2012
  51. 51. unstructured data
  52. 52. free text
  53. 53. Danish
  54. 54. busy doctors
  55. 55. psychiatric patients
  56. 56. delusions
  57. 57. text mining
  58. 58. computer
  59. 59. as smart as a dog
  60. 60. teach it specific tricks
  61. 61. named entity recognition
  62. 62. custom dictionaries
  63. 63. diseases
  64. 64. drugs
  65. 65. adverse drug events
  66. 66. expansion rules
  67. 67. orthographic variation
  68. 68. typos
  69. 69. “negative modifiers”
  70. 70. negations
  71. 71. family members
  72. 72. detailed disease profiles
  73. 73. Roque et al., PLOS Computational Biology, 2011 3262638254947 Assigned codes Text mined codes
  74. 74. comorbidity
  75. 75. Roque et al., PLOS Computational Biology, 2011
  76. 76. patient stratification
  77. 77. Roque et al., PLOS Computational Biology, 2011
  78. 78. cluster characterization
  79. 79. Roque et al., PLOS Computational Biology, 2011
  80. 80. adverse drug reactions
  81. 81. structured data
  82. 82. medication
  83. 83. clinical narrative
  84. 84. possible ADRs
  85. 85. semi-structured data
  86. 86. SPC Summary of Product Characteristics
  87. 87. drug indications
  88. 88. known ADRs
  89. 89. temporal correlation
  90. 90. link drugs to ADRs
  91. 91. complex filtering
  92. 92. Eriksson et al., submitted, 2013
  93. 93. new ADRs
  94. 94. Eriksson et al., submitted, 2013 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
  95. 95. ADR frequencies
  96. 96. Eriksson et al., submitted, 2013
  97. 97. heavily medicated
  98. 98. Eriksson et al., submitted, 2013
  99. 99. ADR dose dependency
  100. 100. Eriksson et al., submitted, 2013
  101. 101. ADR similarity
  102. 102. Eriksson et al., submitted, 2013
  103. 103. drug repurposing
  104. 104. Campillos, Kuhn et al., Science, 2008
  105. 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
  106. 106. Thank you!

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