Text mining, machine learning, NLP and all that (in 10 minutes)
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Byron C Wallace, from #CochraneTech Symposium, Québec 2013
![why do we need this stuff?
[Bastian et al, PLoS Medicine 2010]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Text mining, machine learning, NLP and all that (in 10 minutes)
- 1. text mining, machine learning, NLP and all that (in 10 minutes) Byron C Wallace Brown Center for Evidence Based Medicine #CochraneTech
- 2. why do we need this stuff? [Bastian et al, PLoS Medicine 2010]
- 3. why do we need this stuff? [Bastian et al, PLoS Medicine 2010]
- 5. PubMed ? 2 search database 1 formulate question, protocol & query 4 extract data treatment outcome ba c d 3 screen retrieved citations Studies AIMS1988 ASSET1988 Aber1976 Amery1969 Anderson1983 Bassand1986 Bett1973 Bossaert1987 Brunelli1988 Buchalter1987 Croydon1987 Dewar1963 Durand1987 ECSG−11979 ECSG−21988 EWP1971 Fletcher1959 GISSI1986 Gormsen1973 Guerci1987 Heikinheim1971 ISAM1986 ISISPilot1987 ISIS−21988 Ikram1986 Julian1987 Khaja1983 Leiboff1984 Maublant1988 Meinertz1988 NHFAustra1988 Olson1986 Raizner1985 Rentrop1984 Sainsous1986 Schreiber1986 Simoons1985 TICO1988 Topol1987 WWICSK1983 WWIVSK1988 White1987 Overall (I^2=19% , P=0.147) 0 0.01 0.02 0.04 0.08 0.190.270.38 0.76 1.91 3.82 7.65 18.26 OddsRatio(logscale) 5 synthesize extracted data what can we automate
- 6. PubMed ? 2 search database 1 formulate question, protocol & query 4 extract data treatment outcome ba c d 3 screen retrieved citations Studies AIMS1988 ASSET1988 Aber1976 Amery1969 Anderson1983 Bassand1986 Bett1973 Bossaert1987 Brunelli1988 Buchalter1987 Croydon1987 Dewar1963 Durand1987 ECSG−11979 ECSG−21988 EWP1971 Fletcher1959 GISSI1986 Gormsen1973 Guerci1987 Heikinheim1971 ISAM1986 ISISPilot1987 ISIS−21988 Ikram1986 Julian1987 Khaja1983 Leiboff1984 Maublant1988 Meinertz1988 NHFAustra1988 Olson1986 Raizner1985 Rentrop1984 Sainsous1986 Schreiber1986 Simoons1985 TICO1988 Topol1987 WWICSK1983 WWIVSK1988 White1987 Overall (I^2=19% , P=0.147) 0 0.01 0.02 0.04 0.08 0.190.270.38 0.76 1.91 3.82 7.65 18.26 OddsRatio(logscale) 5 synthesize extracted data what can we automate
- 7. what can we automate?
- 8. learner unlabeled data U expert labeled data L predictive model abstracts from PubMed search doctor conducting review manually screened abstracts SVM how does this work?
- 9. SVMs o x o o o o o o o o x x x x x x xx x xx x support vectors margino
- 10. bag of words1.2 Supervised M achine Learn I am a Nigerian prince writing to you about an inheritance... ... dinner about prince call ... work nigerian yesterday office inheritance ... ... 0 1 1 0 ... 0 1 0 0 1 ... Figure 1.4: The (binary) Bag-of-Words (BoW) representation.
- 11. special considerations for the case of systematic reviews • class imbalance – far fewer relevant than irrelevant abstracts – asymmetric costs sensitivity more important than specificity • reviewer time is scarce and expensive – better models, fewer labels: active learning and dual supervision
- 12. how do we do? “Towards Modernizing the Systematic Review Pipeline: Efficient Updating via Data Mining” Genetics in Medicine 2012
- 13. PubMed ? 2 search database 1 formulate question, protocol & query 4 extract data treatment outcome ba c d 3 screen retrieved citations Studies AIMS1988 ASSET1988 Aber1976 Amery1969 Anderson1983 Bassand1986 Bett1973 Bossaert1987 Brunelli1988 Buchalter1987 Croydon1987 Dewar1963 Durand1987 ECSG−11979 ECSG−21988 EWP1971 Fletcher1959 GISSI1986 Gormsen1973 Guerci1987 Heikinheim1971 ISAM1986 ISISPilot1987 ISIS−21988 Ikram1986 Julian1987 Khaja1983 Leiboff1984 Maublant1988 Meinertz1988 NHFAustra1988 Olson1986 Raizner1985 Rentrop1984 Sainsous1986 Schreiber1986 Simoons1985 TICO1988 Topol1987 WWICSK1983 WWIVSK1988 White1987 Overall (I^2=19% , P=0.147) 0 0.01 0.02 0.04 0.08 0.190.270.38 0.76 1.91 3.82 7.65 18.26 OddsRatio(logscale) 5 synthesize extracted data beyond citation screening
- 14. PubMed ? 2 search database 1 formulate question, protocol & query 4 extract data treatment outcome ba c d 3 screen retrieved citations Studies AIMS1988 ASSET1988 Aber1976 Amery1969 Anderson1983 Bassand1986 Bett1973 Bossaert1987 Brunelli1988 Buchalter1987 Croydon1987 Dewar1963 Durand1987 ECSG−11979 ECSG−21988 EWP1971 Fletcher1959 GISSI1986 Gormsen1973 Guerci1987 Heikinheim1971 ISAM1986 ISISPilot1987 ISIS−21988 Ikram1986 Julian1987 Khaja1983 Leiboff1984 Maublant1988 Meinertz1988 NHFAustra1988 Olson1986 Raizner1985 Rentrop1984 Sainsous1986 Schreiber1986 Simoons1985 TICO1988 Topol1987 WWICSK1983 WWIVSK1988 White1987 Overall (I^2=19% , P=0.147) 0 0.01 0.02 0.04 0.08 0.190.270.38 0.76 1.91 3.82 7.65 18.26 OddsRatio(logscale) 5 synthesize extracted data beyond citation screening