Transcript MELISA An ontology-based agent for information retrieval in medicine

MELISA
An ontology-based agent for
information retrieval in medicine
Jose Maria Abásolo & Mario Gómez
Institut d´Investigaciò en Intel.ligència Artificial (IIIA)
Spanish Scientific Research Council (CSIC)
Index
• Motivation
• Overview
• MELISA process
– Query Generation
– Query Evaluation, Filter & Combination
• Results
• Conclusions
• Future Work
Motivation
• Nowadays Internet gives us a great quantity of information
• Most users find difficult to formulate well-designed
queries for retrieval purposes
• Usually a user makes a first query and then he has to
reformulate the query (one or more times) to get useful
information
• This project try to solve this problem within a professional
domain (biomedical literature)
Overview
1. Input Interface
6. Query
Models
2. Query Generation
7. Medical
Ontology
3. Query Evaluation
8. PubMed
(Medline)
4. Filter & Combination
5. Output Interface
9. MeSH
Browser
(Medline)
Medical Ontology
MEDICAL
CLASS
EVIDENCE
QUALITY
GOOD EVIDENCE QUALITY
MEDIUM EVIDENCE QUALITY
POOR EVIDENCE QUALITY
CLINICAL
CATEGORIES
DIAGNOSIS
THERAPY
PROGNOSYS
ADVERSE EFFECTS
RISK FACTORS
ANALYSIS
EVIDENCE
INTEGRATION
DECISSION TREES
POLICY MAKING
COST ANALYSIS
GUIDELINES
NURSING
EVIDENCE BASED MEDICINE
REVIEW
GUIDELINES is-an-instance-of
EVIDENCE_INTEGRATION
Name: Guidelines
MeSH_Terms: Guidelines, “Practice Guidelines”,
“Clinical Protocol”
Publication_Type: guideline, “practice guideline”
Related_MeSH_Terms: “Guideline Adherence”
Query Model
Consultation
Very abstract,
is given
by the user
Conceptual queries
Specific queries
Link
the consultation
to the ontology
Queries valid
for some
data source
Generation of queries
CONSULTATION
DECOMPOSITION LEVEL 1
CONCEPTUAL
QUERY 1
CONCEPTUAL
QUERY 2
CONCEPTUAL
QUERY N
DECOMPOSITION LEVEL 2
QUERY
QUERY
QUERY
Pneumonia &Ofloxacin
Decomposition Level 1
Good Evidence
Therapy
EBM
Cost Analysis
Guidelines
Decomposition Level 2
Specific Query1
Specific Query2
Specific Query3
….. Specific Query n
SQ1 : pneumonia * ofloxacin AND guidelines [MAJR]
SQ2 : pneumonia * ofloxacin AND guidelines [MH:NOEXP]
SQ3 : pneumonia * ofloxacin AND guidelines [MH]
Query evaluation & combination
•Scoring documents inside a Conceptual Query
•Combine documents from different conceptual queries
Scoring documents inside a
Conceptual Query
SPECIFIC
QUERY
SPECIFIC
QUERY
SPECIFIC
QUERY
SPECIFIC
QUERY
SPECIFIC
QUERY
LIST
UID
Weighted Sum
LIST
UID
LIST
UID
LIST
UID
LIST
UID
LIST
SCORED
UID
CONCEPTUAL QUERY
Combine documents from
different Conceptual Queries
Categories
To
Combine
List of
Documents
Combine documents from
different Conceptual Queries (II)
CONCEPTUAL
QUERY
CONCEPTUAL
QUERY
CONCEPTUAL
QUERY
CONCEPTUAL
QUERY
CONCEPTUAL
QUERY
LIST
SCORED
UID
LIST
SCORED
UID
LIST
SCORED
UID
LIST
SCORED
UID
LIST
SCORED
UID
Aggregation
Function
LIST OF
DOCUMENTS
Results
• Comparison between MELISA and a human user
working with PubMed
• 5 queries (evaluating best 40 documents for any
query)
• For example:
– Human user query
“Osteoporosis AND Women AND (Therapy OR Guideline OR Cost) “
– MELISA
Keywords: Osteoporosis, Women
Selected categories: Therapy, Guideline, Cost analysis
Results (II)
50
45
40
35
30
25
20
15
10
5
0
MELISA
PubMed
2
1
0
?
Conclusions
• The system is able to integrate a big amount of information
and show the results in a dynamic way
• The use of the ontology has two main benefits:
– Helps user to make a consultation
– Allow to use synonymous and related terms
• Our architecture seems to be a good approach to solve the
problem of domain and source independence, but it needs
to be improved
• A great problem is the combination of results from
different categories
• The first empirical test shows that the system improves the
traditional retrieve using PubMed
Future work
•
•
•
•
To develop user profiles
To work with multiple information sources
To study and compare different evaluation functions
To study more complex criteria to reformulate the specific
queries
• To develop algorithms for learning the weight coefficients
• To apply the system in other domains
• To study other query (reformulation) operators
(generalization, specification, source selection)