Pragmatic text mining: From literature to electronic health records
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This document discusses using text mining techniques like named entity recognition and relation extraction on unstructured biomedical text and electronic health records. It describes benchmarking methods for evaluating text mining algorithms by comparing automatic tagging to manual annotations. The document also explains how text mining can help identify known and new adverse drug reactions by linking drugs to adverse events found in clinical notes.
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Pragmatic text mining: From literature to electronic health records
- 1. Lars Juhl Jensen Pragmatic text mining From literature to electronic health records
- 3. data mining
- 9. >10 km
- 10. too much to read
- 11. computer
- 12. as smart as a dog
- 13. teach it specific tricks
- 17. text corpus
- 19. synonyms
- 20. expansion rules
- 24. “black list”
- 25. a
- 26. the formal way
- 27. benchmarking
- 30. compare
- 32. quality metrics
- 33. precision
- 34. recall
- 35. F-score
- 38. not new
- 39. make new corpus
- 40. hard work
- 43. semantic tagging
- 45. Gene and protein names Cue words for entity recognition Verbs for relation extraction [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1]
- 51. random sampling
- 54. precision
- 55. no recall
- 56. relative recall
- 57. compare methods
- 58. co-mentioning
- 59. within documents
- 61. within sentences
- 62. weighted score
- 63. gold standard
- 66. unifying text & data
- 67. text mining
- 71. many sources
- 73. varying quality
- 75. gold standard
- 76. web resources
- 77. protein networks
- 78. string-db.org
- 80. stitch-db.org
- 87. Jensen et al., Nature Reviews Genetics, 2012
- 88. structured data
- 89. Jensen et al., Nature Reviews Genetics, 2012
- 93. Danish
- 94. busy doctors
- 95. typos
- 96. text mining
- 99. drugs
- 100. Clozapine
- 103. hand-crafted rules
- 104. 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
- 105. 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
- 106. 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
- 107. 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
- 108. refind known ADRs
- 109. discover new ADRs
- 110. Acknowledgments Molecular networks Michael Kuhn Damian Szklarczyk Andrea Franceschini Milan Simonovic Alexander Roth Sune Frankild Christian von Mering Peer Bork Localization and disease Sune Frankild Alberto Santos Janos Binder Kalliopi Tsafou Christian Stolte Albert Palleja Heiko Horn Reinhardt Schneider Sean O’ Donoghue Medical text mining Robert Eriksson Peter Bjødstrup Jensen Sune Frankild Thomas Werge Søren Brunak