1. Mining literature and medical records
>10 km
Lars Juhl Jensen

2. exponential growth

5. ~45 seconds per paper

6. outline

7. information retrieval

8. named entity recognition

9. augmented browsing

10. information extraction

11. text corpora

12. web resources

13. electronic health records

14. medical text mining

15. questions

16. information retrieval

17. find the relevant papers

18. ad hoc retrieval

19. user-specified query

20. “yeast AND cell cycle”

21. PubMed

23. indexing

24. fast lookup

25. stemming

26. word endings

27. dynamic query expansion

28. MeSH terms

29. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1
homolog) directly phosphorylated Swe1 and
this modification served as a priming step to
promote subsequent Cdc5-dependent Swe1
hyperphosphorylation and degradation

30. no tool will find that

31. named entity recognition

32. identify the concepts

33. computer

34. as smart as a dog

35. teach it specific tricks

38. comprehensive lexicon

39. small molecules

40. proteins

41. cellular components

42. tissues

43. organisms

44. environments

45. diseases

46. phenotypes

47. behaviors

48. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1
homolog) directly phosphorylated Swe1 and
this modification served as a priming step to
promote subsequent Cdc5-dependent Swe1
hyperphosphorylation and degradation

49. orthographic variation

50. prefixes and postfixes

51. CDC28 vs. Cdc28p

52. Myc vs. c-Myc

53. singular and plural forms

54. noun and adjective forms

55. flexible matching

56. upper- and lower-case

57. spaces and hyphens

58. disambiguation

59. homonyms

60. “black list”

61. unfortunate names

62. SDS

63. a

64. scalable implementation

65. >10 km
<10 hours

66. augmented browsing

67. show relevant information

68. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1
homolog) directly phosphorylated Swe1 and
this modification served as a priming step to
promote subsequent Cdc5-dependent Swe1
hyperphosphorylation and degradation

69. Reflect

70. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
O’Donoghue et al., Journal of Web Semantics, 2010

71. browser add-on

72. Firefox

73. Google Chrome

74. Safari

75. Internet Explorer

76. PDF viewer

77. Utopia Documents

78. web services

80. still too much to read

81. information extraction

82. formalize the facts

83. the starting point

84. named entity recognition

85. two approaches

86. co-mentioning

87. within documents

88. within paragraphs

89. within sentences

90. weighted counts

92. co-mentioning score

93. absolute co-mentionings

94. relative overrepresentation

96. NLP
Natural Language Processing

97. grammatical analysis

98. part-of-speech tagging

99. noun, verb, etc.

100. multiword detection

101. semantic tagging

102. binding, regulation, etc.

103. sentence parsing

104. 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]

105. extract stated facts

106. handle negations

107. high precision

108. poor recall

109. highly domain specific

110. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1
homolog) directly phosphorylated Swe1 and
this modification served as a priming step to
promote subsequent Cdc5-dependent Swe1
hyperphosphorylation and degradation

111. text corpora

112. a body of text

113. most use abstracts

114. few use full-text articles

115. no access

117. ~22 mio. abstracts

118. ~1.8 mio. free articles

119. ~1.4 mio. Elsevier articles

120. ~7.5 mio. patents

121. web resources

122. information on proteins

123. iHOP

126. STRING

127. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011

128. text mining channel

129. what is known

130. not in databases

131. human proteins

133. co-mentioning dominates

134. NLP provides actions

135. homology transfer

136. STITCH

137. small molecules

139. COMPARTMENTS

140. subcellular localization

142. DISEASES

143. human diseases

144. search for a protein

146. search for a disease

148. STRING payload

149. evidence viewers

151. electronic health records

152. what happens at a hospital

153. Jensen et al., Nature Reviews Genetics, 2012

154. two types of data

155. structured data

156. Jensen et al., Nature Reviews Genetics, 2012

157. unstructured data

158. clinical narrative

160. getting access

161. patient consent

162. opt-out

163. opt-in

164. ethical approval

165. medical question

166. no explorative studies

167. data security

168. not anonymized

169. not transferable

170. hospital IT systems

171. not standardized

172. clinical narrative

173. not normal language

174. trouble for NLP

175. in native language

176. not English

177. few tools

178. no dictionaries

179. by busy doctors and nurses

180. typos

181. medical text mining

182. what is possible?

183. a psychiatric corpus

184. clinical narrative

186. in Danish

187. dictionaries

188. diseases

189. drugs

190. adverse drug reactions

191. disease comorbidity

192. Jensen et al., Nature Reviews Genetics, 2012

193. multiple testing

194. comorbidity matrix

195. Roque et al., PLoS Computational Biology, 2011

196. patient clustering

197. Jensen et al., Nature Reviews Genetics, 2012

198. clustering algorithm

199. Roque et al., PLoS Computational Biology, 2011

200. patient stratification

201. temporal correlation

202. drug treatment

203. adverse drug events

204. Eriksson et al., in preparation, 2012

205. pharmacovigilance

206. thank you!

207. questions?