Keynote talk 2020-08-01 for the JCDL Workshop on Conceptual Models: https://sig-cm.github.io/news/JCDL-2020-CFP/ Discussion points: * Methods are a key part of the Knowledge Organizing Structure for Evidence-Based Medicine. * Methods relate to how we GENERATE evidence. * Different methods generate evidence of different kinds and strength. * I believe Methods can be useful in mining claims and arguments from papers: methods AUTHORIZE claims. * More specialized hierarchies of evidence can be found in medicine * Various groups are complicating the “evidence pyramid” hierarchy of evidence.

1. Methods Pyramids as a Knowledge Organizing
Structure for Evidence-Based Medicine
Jodi Schneider
School of Information Sciences
University of Illinois at Urbana-Champaign
Keynote for JCDL 2020 Workshop on Conceptual Modeling, Virtual, 2020-08-01

2. These are some preliminary thoughts on
METHODS as a meso-level organizing structure in
science.

3. Macro Organization of Science

4. Topical Organization of a Field
http://scimaps.org/mapdetail/being_a_map_of_physi_171

5. http://scimaps.org/mapdetail/map_of_complexity_sc_154/
Topic evolution of a field over time

6. Micro Organization of Science

7. Paper Structure
Introduction
Methods
Results
and/Analysis
Discussion

8. Teufel, S. (2014). Scientific Argumentation Detection as Limited-domain Intention Recognition. In
ArgNLP 2014 Frontiers and Connections between Argumentation Theory and Natural Language
Processing. http://ceur-ws.org/Vol-1341/paper14.pdf
Rhetorical Goals

9. Argument Modeling
Fu & Schneider, JCDL 2020

10. Argument Modeling
Fu & Schneider, JCDL 2020

11. Argument Modeling
Fu & Schneider, JCDL 2020

12. Meso Organization of Science

13. Argument Modeling
Fu & Schneider, JCDL 2020

14. Methods in Evidence-Based Medicine

15. Figure credit: Duke University Medical Center Library. Introduction to
Evidence-based Practice. What is Evidence-Based Practice (EBP)?
http://guides.mclibrary.duke.edu/c.php?g=158201&p=1036021
“Best Research Evidence”
in Evidence Based Practice

16. Hierarchy of Evidence

17. “The Evidence Pyramid”
Figure credit: SUNY Downstate Medical Center. Medical Research Library of Brooklyn.
Evidence Based Medicine Course. A Guide to Research Methods: The Evidence
Pyramid: http://library.downstate.edu/EBM2/2100.htm

18. “Research design pyramid”
Steele & Tiffin,‘‘Personalised evidence’ for personalised healthcare: integration of a
clinical librarian into mental health services – a feasibility study, The Psychiatric
Bulletin 38(1) (2014), 29–35. doi:10.1192/pb.bp.112.042382
“Research design pyramid, with
highlighted levels indicating the designs
which featured in studies synthesised by
the clinical librarian to provide an
answer to evidence request 6 (broader
clinical). "

19. “Circle of methods”
Tugwell, Peter, and J. André Knottnerus. 2015. “Is the ‘Evidence-Pyramid’ Now Dead?”
Journal of Clinical Epidemiology 68 (11): 1247–50.
https://doi.org/10.1016/j.jclinepi.2015.10.001
“Circle of methods. Experimental
methods that test specifically for
efficacy (upper half of the circle) have to
be complemented by observa- tional,
non-experimental methods (lower half
of the circle) that are more descriptive
in nature and describe real-life effects
and applicability. Shad- ing indicates the
complementarity of experimental and
quasi-experimental methods, of internal
and external validity"

20. “wavy lines” & “a lens” for viewing evidence
Murad, Hassan, Alsawas, and Alahdab. 2016. “New Evidence Pyramid.”
BMJ Evidence-Based Medicine 21 (4): 125–27.
https://doi.org/10.1136/ebmed-2016-110401
“Revising the pyramid: (1)
lines separating the study
designs become wavy
(Grading of
Recommendations
Assessment, Development
and Evaluation), (2)
systematic reviews are
‘chopped off’ the pyramid.
(C) The revised pyramid:
systematic reviews are a lens
through which evidence is
viewed (applied). “

21. Methods as an organizing structure for DOING
clinical science research
• Epidemiology textbooks classify clinical studies as either
observational or experimental and as either prospective or
retrospective.
• Specific study designs are widely agreed upon too: case reports
and case series, case-control studies, cohort studies, and
randomized controlled trials.

22. Methods as an organizing structure for REPORTING
clinical science research
• They frequently appear in research paper titles and are the
subject of reporting guidelines
• Trials: CONSORT (Begg et al. 1996).

23. Methods as an organizing structure for REPORTING
clinical science research
• They frequently appear in research paper titles and are the
subject of reporting guidelines
• Observational studies: STROBE (Vandenbroucke et al. 2007)

24. Methods as an organizing structure for SEARCHING
clinical science research
• Each design has a Medical Subject Heading (MeSH) which can be
used for retrieval.
• Information retrieval groups make search filters (McKibbon et al.
2009 compared 38 randomized controlled trial filters).
• Current work in my group evaluating machine learning tools for
30+ medical types, with training data from MEDLINE/PubMed.

25. Some of our best-performing models
model AUC
BIOGRAPHY 1.00
SYSTEMATIC REVIEW 1.00
CASE REPORTS 0.99
CONSENSUS DEVELOPMENT CONFERENCE 0.99
CROSS OVER STUDY 0.99
DOUBLE BLIND METHOD 0.99
FOCUS GROUPS 0.99
GENOME WIDE ASSOCIATION STUDY 0.99
HUMAN EXPERIMENTATION 0.99
INTERVIEW 0.99
META ANALYSIS 0.99
PORTRAITS 0.99
PRACTICE GUIDELINE 0.99
TWIN STUDY 0.99
AUTOBIOGRAPHY 0.98
Aaron Cohen, Neil Smalheiser, Jodi Schneider –
Text Mining Pipeline to Accelerate Systematic Reviews in Evidence-Based Medicine
AUC = area under the curve
It’s the probability that a
classifier will rank a
randomly chosen positive
instance higher than a
randomly chosen negative
one.

26. Taking it further

27. Boyce, R.D.: A Draft Evidence Taxonomy and Inclusion Criteria for the Drug Interaction
Knowledge Base (DIKB),
http://purl.net/net/drug-interaction-knowledge-base/evidence-types-and-inclusion-criteria
Finer grained evidence hierarchies

28. Work in progress detecting finer grained methods
Hoang, Boyce, Brochhausen, Utecht, Schneider. “A proposal for determining the evidence types of biomedical documents using a
drug-drug interaction ontology and machine learning.” Poster at AAAI 2019 Spring Symposium on Combining Machine Learning
with Knowledge Engineering (AAAI-MAKE 2019).

29. Connecting all this to Knowledge Organization
(explicitly)

30. Integrative Levels
Kleineberg, Michael. 2017. “Integrative Levels.” Knowledge Organization 44 (5):
349–79. https://doi.org/10.5771/0943-7444-2017-5-349

31. Domains are constituted of…
Hjørland, Birger, and Jenna Hartel. 2003. “Afterword: Ontological, Epistemological
and Sociological Dimensions of Domains.” Knowledge Organization 30 (3/4): 239–45.
(1) ontological theories and concepts about the objects of human
activity;
(2)epistemological theories and concepts about knowledge and
the ways to obtain knowledge, implying methodological
principles about the ways objects are investigated; and
(3) sociological concepts about the groups of people concerned
with the objects.

32. Discussion Points
• Methods are a key part of the Knowledge Organizing Structure for
Evidence-Based Medicine.
• Methods relate to how we GENERATE evidence.
• Different methods generate evidence of different kinds and strength.
• I believe Methods can be useful in mining claims and arguments from
papers: methods AUTHORIZE claims.
• More specialized hierarchies of evidence can be found in medicine
• Various groups are complicating the “evidence pyramid” hierarchy of
evidence.

33. Conceptual Modeling in the Main Conference!
• Are papers citing a retracted paper
necessarily wrong?
• Does it matter when citing authors make
use of a paper whose findings are no longer
considered valid?
• When DOES the citation matter?
• Could we selectively alert authors who cite a
retracted or abandoned paper?
Yuanxi Fu, Jodi Schneider. “Towards knowledge maintenance in scholarly digital libraries with keystone citations.”
doi:10.1145/3383583.3398514 Preprint: http://jodischneider.com/pubs/jcdl2020.pdf

34. Under our framework:
1) A scientific research paper puts forward at
least one main finding, along with a
logical argument, giving reasons and
evidence to support the main finding.
2) The main finding is accepted (or not) on
the basis of the logical argument.
3) Evidence from earlier literature may be
incorporated into the argument by citing a
paper and presenting it as support, using a
citation context.
Main Finding
Arguments
support
support
Data Methods Citations …
Citing ArticleCited Article
Citation Context
“Many papers with
known validity problems
are highly cited [3].”
Our
paper
[3] = Bar-Ilan, J.
and Halevi, G.
2018. Temporal
characteristics
of retracted
articles

35. References
Begg, C., M. Cho, S. Eastwood, R. Horton, D. Moher, I. Olkin, R. Pitkin, et al. “Improving the Quality of Reporting of Randomized Controlled Trials. The CONSORT Statement.” JAMA 276, no. 8 (August 28,
1996): 637–39. https://doi.org/10.1001/jama.276.8.637
Boyce, Richard. “A Draft Evidence Taxonomy and Inclusion Criteria for the Drug Interaction Knowledge Base (DIKB),” April 26, 2015. https://web.archive.org/web/20150426205701/http://dbmi-icode-
01.dbmi.pitt.edu/dikb-evidence/just-inclusion-criteria/just-inclusion-criteria.html
Brochhausen, Mathias, William R Hogan, Philip E Empey, Jodi Schneider, and Richard D Boyce. “Adding Evidence Type Representation to DIDEO.” In Proceedings of the Joint International Conference on
Biological Ontology and BioCreative Corvallis, Oregon, United States, August 1-4, 2016, 2, 2016. http://ceur-ws.org/Vol-1747/IP02_ICBO2016.pdf
Fu & Schneider. “Towards knowledge maintenance in scholarly digital libraries with keystone citations.” In JCDL 2020. Pages 217–226. https://doi.org/10.1145/3383583.3398514
Grizzle, Amy J., John Horn, Carol Collins, Jodi Schneider, Daniel C. Malone, Britney Stottlemyer, and Richard David Boyce. “Identifying Common Methods Used by Drug Interaction Experts for Finding
Evidence about Potential Drug-Drug Interactions: Web-Based Survey.” Journal of Medical Internet Research 21, no. 1 (2019): e11182. https://doi.org/10.2196/11182
Hjørland, Birger, and Jenna Hartel. 2003. “Afterword: Ontological, Epistemological and Sociological Dimensions of Domains.” Knowledge Organization 30 (3/4): 239–45.
Hoang, Boyce, Brochhausen, Utecht, Schneider. “A proposal for determining the evidence types of biomedical documents using a drug-drug interaction ontology and machine learning.” Poster at AAAI 2019
Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). http://jodischneider.com/pubs/aaaimake2019.pdf
Kleineberg, Michael. 2017. “Integrative Levels.” Knowledge Organization 44 (5): 349–79. https://doi.org/10.5771/0943-7444-2017-5-349
Murad, Hassan, Alsawas, and Alahdab. 2016. “New Evidence Pyramid.” BMJ Evidence-Based Medicine 21 (4): 125–27. https://doi.org/10.1136/ebmed-2016-110401
Romagnoli, Katrina M., Scott D. Nelson, Lisa Hines, Philip Empey, Richard D. Boyce, and Harry Hochheiser. “Information Needs for Making Clinical Recommendations about Potential Drug-Drug Interactions:
A Synthesis of Literature Review and Interviews.” BMC Medical Informatics and Decision Making 17, no. 1 (February 22, 2017): 21. https://doi.org/10.1186/s12911-017-0419-3
Schneider, Jodi, and Sally Jackson. “Modeling the Invention of a New Inference Rule: The Case of ‘Randomized Clinical Trial’ as an Argument Scheme For Medical Science.” Argument & Computation 9, no. 2
(January 1, 2018): 77–89. https://doi.org/10.3233/AAC-180036
Steele & Tiffin,‘‘Personalised evidence’ for personalised healthcare: integration of a clinical librarian into mental health services – a feasibility study, The Psychiatric Bulletin 38(1) (2014), 29–35.
https://doi.org/10.1192/pb.bp.112.042382
Teufel, S. (2014). Scientific Argumentation Detection as Limited-domain Intention Recognition. In ArgNLP 2014 Frontiers and Connections between Argumentation Theory and Natural Language Processing.
http://ceur-ws.org/Vol-1341/paper14.pdf
Tugwell, Peter, and J. André Knottnerus. 2015. “Is the ‘Evidence-Pyramid’ Now Dead?” Journal of Clinical Epidemiology 68 (11): 1247–50. https://doi.org/10.1016/j.jclinepi.2015.10.001
Vandenbroucke, Jan P., Erik Von Elm, Douglas G. Altman, Peter C. Gøtzsche, Cynthia D. Mulrow, Stuart J. Pocock, Charles Poole, James J. Schlesselman, and Matthias Egger. “Strengthening the Reporting of
Observational Studies in Epidemiology (Strobe): Explanation and Elaboration.” Annals of Internal Medicine 147, no. 8 (2007): W–163.