The pragmatic text miner: It’s just another type of poorly standardized data
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This document provides an overview of text mining techniques used to analyze unstructured biomedical text and integrate extracted information with structured data. Key applications discussed include named entity recognition to identify biological entities in text, methods to determine relationships between entities mentioned in close proximity, and approaches to identify adverse drug events reported in clinical notes. The document also references several online resources and databases generated using these text mining methods that integrate extracted information with existing structured data.
The pragmatic text miner: It’s just another type of poorly standardized data
- 1. Lars Juhl Jensen The pragmatic text miner It’s just another type of poorly standardized data
- 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. co-mentioning
- 27. within documents
- 29. within sentences
- 30. weighted score
- 31. unifying text & data
- 32. text mining
- 36. protein networks
- 37. Szklarczyk et al., Nucleic Acids Research, 2015string-db.org
- 39. Kuhn et al., Nucleic Acids Research, 2014stitch-db.org
- 41. Binder et al., Database, 2014compartments.jensenlab.org
- 43. tissues.jensenlab.org Santos et al., submitted, 2015
- 45. diseases.jensenlab.org Frankild et al., Methods, 2015
- 46. many databases
- 49. variable quality
- 50. not comparable
- 51. hard work
- 53. quality scores
- 54. calibrate vs. gold standard
- 55. von Mering et al., Nucleic Acids Research, 2005
- 60. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009reflect.ws
- 62. Jensen et al., Nature Reviews Genetics, 2012
- 63. structured data
- 64. Jensen et al., Nature Reviews Genetics, 2012
- 67. distributions
- 68. Jensen et al., Nature Communications, 2014
- 69. trajectories
- 70. Jensen et al., Nature Communications, 2014
- 74. Danish
- 75. busy doctors
- 78. drugs
- 79. Clozapine
- 82. 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
- 83. 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
- 84. 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
- 86. Acknowledgments STRING/STITCH Michael Kuhn Damian Szklarczyk Andrea Franceschini Milan Simonovic Alexander Roth Sune Pletscher-Frankild Jianyi Lin Pablo Minguez Christian von Mering Peer Bork Text mining Sune Pletscher- Frankild Jasmin Saric Evangelos Pafilis Alberto Santos Janos Binder Kalliopi Tsafou Heiko Horn Michael Kuhn Reinhardt Schneider Sean O’ Donoghue EHR mining Anders Boeck Jensen Robert Eriksson Peter Bjødstrup Jensen Andreas Bok Andersen Sabrina Gade Ellesøe Henriette Schmock Tudor Oprea Pope Moseley Thomas Werge Søren Brunak