1. Intelligent Information Directory System for Clinical Documents Qinghua Zou 6/3/2005 Dr. Wesley W. Chu (Advisor)

2. When searching clinical reports  Keyword Search  Problems Hard to compose good keywords Lack an outlook of the content Interchangeable words

3. Intelligent Directory System  1. Overview  2. Extracting Key Concepts  3. Mining Topics  4. Building Directories  5. Searching  6. Conclusion

4. 1. System Overview

5. 2. Concept Extraction  2.1 Introduction  2.2 Our approach: IndexFinder Index Phase (Offline) Search Phase (Real Time)  2.3 Experiments  2.4 Summary

6. 2.1 Motivation  Clinical texts are valuable in medical practice Search relevant reports Search similar patients  What is key information?  UMLS provides key medical concepts  Our Goal Extract UMLS concepts from clinical texts Clinical Texts Extract key info. Standard terms

7. 2.1 Previous Approaches Free text ip dpi1 i0vplambs will v0 eat dp oats NLP Parser UMLS Mapping UMLS Concepts Noun phrases lambs oats

8. 2.1 Problems of Previous Approaches  Concepts cannot be discovered if they are not in a single noun phrase. E.g. In “second, third, and fourth ribs”, “Second rib” can not be discovered.  Difficult to scale to large text computing. Natural language processing requires significant computing resources

9. 2.2 Our Approach: IndexFinder Free text NLP Parser Noun phrases UMLS MappingConcepts We would discard all words in the text except “lung” and “cancer”. Our approach: UMLS  free textPrevious: free text  UMLS Suppose UMLS contains only “Lung cancer” Indexing Index Data ~80MB UMLS 2GB Index phase (offline) conceptsFiltering Extracting Free text Search phase (real time)

10. 2.2 Our Approach: What’s New?  Knowledge-based approach Using the compact index data without using any database system Permuting words in a sentence to generate UMLS concept candidates. Using filters to eliminate irrelevant concepts.

11. 2.2 Concept Candidates Generation Assumptions  Knowledge base provides a phrase table.  Each phrase (concept) is a set of words.  An input text T is represented as a set of words. Goal  Combining words in T to generate concept candidates Example  T={D,E,F} Answer: 5

12. 2.2 Search Phase: Filtering Use filters to eliminate irrelevant concepts  Syntactic filter: Word combination is limited within a sentence.  Semantic filter: Filter out irrelevant concepts using semantic types (e.g. body part, disease, treatment, diagnose). Filter out general concepts using the ISA relationship and keep the more specific ones.

13. 2.3 Experiment Comparison with MetaMap [3] Input: A small mass was found in the left hilum of the lung. MetaMap IndexFinder

14. 2.4 Summary  An efficient method that maps from UMLS to free text for extracting concepts without using any database system.  Syntactic and semantic filters are used to eliminate irrelevant candidates.  IndexFinder is able to find more specific concepts than NLP approaches.  IndexFinder is scalable and can be operated in real time.

15. 3. Mining Topics: SmartMiner  3.1 Introduction  3.2 Search Space  3.3 SmartMiner  3.4 Experiment  3.5 Summary

16. 3.1 Introduction  A Topic (assumption) a set of concepts a frequent pattern  Finding topics by data mining Frequent patterns, or Maximal frequent patterns  Require efficient data mining

17. 3.1 Data Mining Problem 1: a b c d e 2: a b c d 3: b c d 4: b e 5: c d e id: item set Dataset MinSup=2 MFI abcd, be, cde What itemsets are frequent itemsets (FI)? a, b, c, d, e, ab, ac, ad, bc, bd, be, cd, ce, de, abc, abd, acd, bcd, cde, abcd Maximal frequent itemset(MFI): No superset is frequent.

18. 3.1 Why MFI not FI?  Mining FI is infeasible when there exists long FI. E.g, Suppose we have a 20-item frequent set a 1 a 2 … a 20. All of its subset are frequent, i.e., 2 20 =1,048,576  Mining MFI is fast and we can generate all the FI.

19. 3.1 Previous work  Superset checking. A study shows that CPU spends 40% time for superset checking.  Search tree is too large  A large number of support counting  Need more efficient method

20. 3.2 Search space 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: :abcde simplify Ø:abcde What is the space of ?ab:cd ab, abc, abd, abcd

21. 3.2 Space decomposition For a space :abcde, if abcg is frequent,  Then, the known space any subset of abc is frequent known space is :abc  The unknown space are: Any itemsets contain d or e. d:abce and e:abc  :abcde = d:abce + e:abc + :abc

22. 3.3 The basic idea (b) SmartMiner Strategy SmartMiner takes advantages of the information from previous steps. (a) Previous approach B2B2 … A1A1 B1B1 … Creating B 2 before exploring B 1 BnBn B’ … A1A1 B1B1 … Creating B’ after exploring B 1 Using information from B to prune the space at B’

23. 3.3 The tail information  For the space :abcde, if we know abcf, abcg and abfg are frequent, then we project them to the space. abcf  abc. abcg  abc. abfg  ab.  Thus Tinf(abcf,abcg, abfg|:abcde)={abc}

24. 3.4 Running time on Mushroom

25. 3.5 Summary  SmartMiner uses tail information to guide the mining, efficient since A smaller search tree. No superset checking. Reduces the number of support counting.

26. 4. Building Directories  4.1 Introduction  4.2 Knowledge Hierarchies  4.3 User Specification  4.4 Directory Generation  4.5 Integration various directories  4.6 Summary

27. 4.1 Introduction  Three Inputs Topics  Key Content Knowledge trees  Meaningful User specs  Customized

28. 4.2 Knowledge Hierarchies  UMLS concept hierarchies PA: parent-child relationship RA: rather-than relationship  Problems A concept: several parents, different granularity  [lung cancer]  [Neoplasms, Respiratory Tract]  [lung cancer]  [Neoplasms, Respiratory System] A concept: hundreds of paths to roots  [lung cancer]: 233 different paths in UMLS by PA

29. 4.2 Select Proper Hierarchies  Set source preference order, e.g [disease]: ICD9>SNOMED>MeSH [body part]: SNOMED>ICD9  Select proper granularity C: a set of concepts; n: a path node Score function for selecting the node n  S(n)=|{c i | c i  n, c i in C}|  Expert review

30. 4.3 User Specifications  A good directory ~ usage pattern  User spec  usage pattern  User may have different specs  A spec: a series of knowledge names [disease] + [body part], or [body part] + [disease]  Build a directory for a spec by the ordering

31. 4.4 Directory Generation An example User spec 1: d + p [disease] + [body part] User spec 2: p + d [body part] + [disease]

32. 4.4 ~ An example d + p p + d 1 11 1 111 1

33. 4.4 ~ Algorithm

34. 4.5 Integration various directories  For each D i, get all dir paths to D i  A D i is tree: XML Key words can associate with tree nodes Query: xpath  Exist redundant information

35. 4.5 simplified model  Keep only the first level knowledge trees  For //d 6 //p 6, we use XPath query //doc[//d 6 and //p 6 ]  Size smaller, require some computation

36. 4.6 Summary  Build directory by Topics Knowledge hierarchies User specifications  Mapping directories to XML By collecting directory paths for each document Leverage on existing XML technologies