1. Lindsay & Gordon’s Discovery Support Systems Model
John MacMullen
SILS Bioinformatics Journal Club
Fall 2002

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Background
Specialization in science leads to fragmented, ‘complementary but disjoint’ or ‘non-interactive’ literatures
Attempt to find ‘undiscovered public knowledge’ in the biomedical literature
Tools are needed to integrate biomedical knowledge; ‘discovery support systems’ are one class
Replication and extension of Swanson & Smalheiser’s model (‘Arrowsmith’)
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3. Complementary but Disjoint Literatures
Fish Oil C
Raynaud’s
B1 – Blood Viscosity
B2 – Platelet Aggregation
B3 – Vascular Reactivity
‘non-interactive’ literatures
Adapted from Swanson & Smalheiser,
1997 by Weeber, et al., 2001
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4. Swanson & Smalheiser’s method
Systematic trial-and-error method
Procedure 1: Citation acquisition
Search MEDLINE for topical cites (‘C’ list)
Apply stopword list and extract unique terms (‘B’ list)
Search MEDLINE for ‘B’ term cites; prune list
Perform MEDLINE searches for each ‘B’ term
Classify results into likely categories
Derive the intersection of each ‘B’ set with the restriction set, and the union of intersection sets (‘U’)
Search the resulting terms of ‘U’ set in MEDLINE
‘U’ list becomes potential ‘A’ terms, with each ‘A’ term attached to the ‘B’ term that generated it
Rank ‘A’ term results against ‘B’ co-occurrence
Procedure 2: Relationship Mining
Search for pre-existing A→C &/or A→B→C relationships
Search for novel A→C relationships
Output: Display of ‘A’ & ‘C’ cites by their common ‘B’ terms
Goal: a plausible testable hypothesis
Human relevance judgments in each step influence future steps
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5. Lindsay & Gordon’s method
Limited to lexical statistics, no syntactic or semantic evaluation
Uses full MEDLINE record instead of title only
Identify a source literature (e.g., a topic)
Find all single words, bi-grams & tri-grams in source corpus; exclude stop words; normalize singular/plural
Calculate 4 statistics for each token (tf, df, rf, tf*idf)
Rank tokens by frequency of occurrence
Identify 1 or more intermediate literatures based on stats in step (3), starting with highest ranks from (4)
Run process from steps 1-4 for each intermediate literature
DL complete MEDLINE records for all docs “mentioning” (i.e., word match or index term) source topic
General stop word list + common medical terminology
Lindsay & Gordon, 1999, pp
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6. Term / Phrase Statistics
tf = token frequency in sub-corpus
df = doc frequency (# of docs in sub-corpus w/this term)
rf = relative frequency (# of appearances in sub-corpus vs whole corpus)
idf(t) = log(N / f(t)) where N = # of docs in whole corpus over number of docs with t in them
tf*idf = token frequency * inverse doc frequency
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Experiments
Reproduction of Swanson & Smalheiser’s magnesium / migraine connection
Used their method to try to find the 11 intermediate literatures S&S found +1 new
1,081 MEDLINE records from on migraine
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Outcome
Is ranking tokens by frequency effective?
Arbitrary ranking and population size cutoffs
Domain knowledge used (med student)
Lots of manual intervention. Is this reproducible? Is it really all that different from S&S?
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Hypotheses
Intermediate literatures are best identified by absolute lexical frequencies
Candidate discoveries are best generated by relative lexical frequencies
Results showed the opposite of H2 ( )
Search for the absence of connections between source and target literatures (Swanson’s disjointness)
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Questions
Is this process more automatable?
Is 1 intermediate step enough? What happens to system complexity when there are i intermediate literatures? S I1 Ii T
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