Intelligent Information Directory System for Clinical Documents. Qinghua Zou 6/3/2005. Dr. Wesley W. Chu (Advisor). Keyword Search Problems Hard to compose good keywords Lack an outlook of the content Interchangeable words. When searching clinical reports. Intelligent Directory System.
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Intelligent Information Directory System for Clinical Documents
Dr. Wesley W. Chu (Advisor)
Hard to compose good keywords
Lack an outlook of the content
Our approach: UMLSfree text
Previous: free textUMLS
Suppose UMLS contains only
We would discard all words in the text except “lung” and “cancer”.
2.2 Concept Candidates Generation
Use filters to eliminate irrelevant concepts
2.3 Experiment Comparison with MetaMap 
Input:A small mass was found in the left hilum of the lung.
id: item set
a, b, c, d, e,
1: a b c d e
2: a b c d
3: b c d
4: b e
5: c d e
ab, ac, ad, bc, bd, be, cd, ce, de,
abc, abd, acd, bcd, cde,
What itemsets are frequent itemsets (FI)?
Maximal frequent itemset(MFI):
No superset is frequent.
abcd, be, cde
What is the space of ?
Given 5 items: a, b, c, d, e.
What is the search space?
Ø, a, b, c, d, e, ab, ac, ad, ae, bc, …, abcde
We use “head:tail” to denote the space as:
ab, abc, abd, abcd
For a space :abcde, if abcg is frequent,
Creating B2 before exploring B1
Creating B’ after exploring B1
Using information from B to prune the space at B’
(b) SmartMiner Strategy
(a) Previous approach
SmartMiner takes advantages of the information from previous steps.
User spec 1: d + p
[disease] + [body part]
User spec 2: p + d
[body part] + [disease]
d + p
p + d
For each Di, get all dir paths to Di
A Di is tree: XML
Key words can associate with tree nodes
Exist redundant information
//doc[//d6 and //p6]