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Medical data and text mining: Linking diseases, drugs, and adverse reactions

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Medical data and text mining: Linking diseases, drugs, and adverse reactions

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Medical data and text mining: Linking diseases, drugs, and adverse reactions

  1. 1. Medical data and text mining Linking diseases, drugs, and adverse reactions Lars Juhl Jensen
  2. 2. structured data
  3. 3. Jensen et al., Nature Reviews Genetics, 2012
  4. 4. central registries
  5. 5. unstructured data
  6. 6. individual hospitals
  7. 7. opt-out
  8. 8. opt-in
  9. 9. diagnosis trajectories
  10. 10. Danish registries
  11. 11. civil registration system
  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. not research
  23. 23. reimbursement
  24. 24. comorbidity
  25. 25. naïve approach
  26. 26. contingency table
  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., Nature Communications, 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. proxy diagnoses
  41. 41. temporal correlations
  42. 42. diagnosis 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. pharmacovigilance
  50. 50. clinical trials
  51. 51. spontaneous reports
  52. 52. underreporting
  53. 53. data mining
  54. 54. structured data
  55. 55. Jensen et al., Nature Reviews Genetics, 2012
  56. 56. unstructured data
  57. 57. free text
  58. 58. Danish
  59. 59. busy doctors
  60. 60. typos
  61. 61. psychiatric patients
  62. 62. text mining
  63. 63. computer
  64. 64. as smart as a dog
  65. 65. teach it specific tricks
  66. 66. comprehensive dictionary
  67. 67. adverse drug reactions
  68. 68. drugs
  69. 69. Clozapine
  70. 70. Clozapine clozapi n clossapi n klozapin e chlosapi n chlosapi ne chlozapi n chlozapi ne klossapi n closapin e klozapi nklosapi n
  71. 71. temporal correlations
  72. 72. hand-crafted rules
  73. 73. Eriksson et al., Drug Safety, 2014 Drug introduction Drug discontinuationAdverse event Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction ADR of additional drug
  74. 74. Eriksson et al., Drug Safety, 2014 Drug introduction Drug discontinuationAdverse eventIdentification start Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction ADR of additional drug
  75. 75. Eriksson et al., Drug Safety, 2014 Drug introduction Drug discontinuation Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction Adverse event ADR of additional drug Identification start
  76. 76. Eriksson et al., Drug Safety, 2014 Drug introduction Drug discontinuation Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction Adverse event ADR of additional drug Identification start
  77. 77. recall known ADRs
  78. 78. discover new ADRs
  79. 79. estimate ADR frequencies
  80. 80. 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
  81. 81. ありがとうございます

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