1. Information Retrieval
Introduction to Biomedical Informatics
10/27/2015
Information Retrieval

2. The Problem Set of Journal Articles Information Need
What strategies would you use to get the journal articles that meet your information need?

3. What is Information Retrieval
Activity of obtaining information resources relevant to an information need from a collection of information resources.
Field concerned with the organization, storage, and retrieval of information.
Related Topics: Information Extraction, Natural Language Processing

4. Examples

5. Examples

6. Examples

7. Examples

8. Is It a database?

9. Comparison Database vs IR
Data Format
Database: Structured Data. Has a schema.
IR: Unstructured Documents
Queries
Database: Language like Structured Query Language
Ex: SELECT Cust_No, First_Name FROM Customers WHERE Last_Name='Smith';
IR: Keywords or Natural Language
Ex: causes of diabetes
Result
Database: Exact Result
IR: Ranked Results (Hit or Miss)

10. Specific Definition of IR
Objective: Find documents relevant to a query.
Documents
Relevance
Query

11. Information Retrieval Types
Ad-hoc
Find documents in a static collection.
Ex: Articles about Diabetes in Pubmed.
Routing
Route documents as they come to groups.
Ex: Is this about work or personal?
Filtering
Remove irrelevant documents as they come.
Ex: spam or not spam?

12. Types Question Answering Image Recognition Social Search Geo search
What are the causes of lung cancer?
Ex: smoking, asbestos
Image Recognition
What does a melanoma look like.
Social Search
Geo search

13. The Information Retrieval Cycle
Source
Selection
Resource
Query
Formulation
Query
query reformulation,
relevance feedback
Search
Ranked List
Selection
Documents
result
Slide is from Jimmy Lin’s tutorial

14. Information Need/ Query Formulation

15. Unrecognized Needs Photocoagulation in diabetic retinopathy (1979)
2 years after publication. How many PCPs were aware of the result?
50%
Hypertension and Follow-Up Study (1981)
2-6 months after publication. How many physicians knew the benefit of antihypertensive therapy?
20 to 50%

16. Recognized Needs
Various studies measured recognized needs per patient.
2 unmet needs for every 3 patients (Covell).
1.4 questions per patient (Osheroff).
0.33 questions per patient (Dee and Blazek)
0.32 questions per patient (Ely)

17. Recognized Needs Ely taxonomized questions (top 10)
What is the cause of symptom X?
What is the dose of drug X?
How should I manage disease or finding X?
How should I treat finding or disease X?
What is the cause of physical finding X?
What is the cause of test finding X?
Could this patient have disease or condition X?
Is test X indicated in situation Y?
What is the drug of choice for condition X?
Is drug X indicated in situation Y?

18. Pursing a Need 30-36% of questions were pursued by physicians.
Factors Most Associated? (Gorman 95)
Urgency
Answerability
Generalizability
Factors Not Associated?
Knowledge
Uneasiness (of physician)
Potential Help (an answer could help the patient)
Potential Harm (not having an answer could hurt the patient)
Edification
Liability (problem involved liability risk)
Knowledge of peers (peers know the answer)
Difficulty (how difficult to find the answer)

19. Obstacles to answering clinical questions (Ely).
Excessive time required to find information.
Difficulty modifying the original question, vague or open to interpretation.
Difficulty selecting an optimal strategy to search for information.
Failure of a seemingly Appropriate resource to cover the topic.
Uncertainty about how to know when all the relevant evidence has been found so the search can stop.
Inadequate synthesis of multiple bits of evidence into a clinically useful statement.

20. Reasons for not pursing an answer (Ely)
Doubted existence of relevant information – 25%
Readily available consultation leading to referral rather than pursuit – 22%
Lack of time to pursue – 19%
Not important enough to pursue answer – 15%
Uncertain where to look for answer – 8%

21. The Information Retrieval Cycle
Source
Selection
Resource
Query
Formulation
Query
query reformulation,
relevance feedback
Search
Ranked List
Selection
Documents
result
Slide is from Jimmy Lin’s tutorial

22. The IR Black Box Documents Query Results
Slide is from Jimmy Lin’s tutorial

23. Inside The IR Black Box Index Documents Query Representation
Document Representation
Index
Comparison
Function
Results
Slide is from Jimmy Lin’s tutorial

24. The Central Problem in IR
Information Seeker
Authors
Concepts
Concepts
Query Terms
Document Terms
Do these represent the same concepts?

25. What Makes IR Hard? Text is Unstructured. Nuance in Language Ambiguity
Is mastectomy the best treatment for breast cancer?
Ambiguity
APC, adenomatous polyposis coli, age period cohort
direct bilirubin (lab value, substance, result).

26. Classic Information Retrieval

27. IR steps Index the Documents. Process the Query.
Score the Documents According to the Query.
Show Results.

28. Indexing: Tokenize Documents
Information
Retrieval Is
This
This is a document in information retrieval
9/22/2018
Introduction to Information Retrieval

29. Indexing: Remove stopwords
Very frequent words are not good discriminators
list of stop words
a, about, above, according, across, after, afterwards, again, against, albeit, all, almost, alone, already, also, although, always, among, as, at
9/22/2018
Introduction to Information Retrieval

30. Indexing: Collapse word forms
Simplest: suffix stripping
smoke  smok
smoker  smok
smoking  smok
9/22/2018
Introduction to Information Retrieval

31. Inverted Index Example
Doc 1
Dictionary
Postings
This is a sample
document
with one sample
sentence
Term
# docs
Total freq
This 2 is
sample 3
another 1 …
Doc id
Freq 1 2 …
Doc 2
This is another
sample document
Slide is from ChengXiang Zhai
9/22/2018
Introduction to Information Retrieval

32. Query Processing. Same process as indexing. Stop word removal Stemming
Want to get document to the same form as indexed version.

33. Scoring Documents Boolean Model
Documents which have more query words get scored higher.
Simple, not very effective.

34. Scoring Documents
Vector Space Model

35. Evaluation Gold Standard Recall (aka Sensitivity) Precision
Proportion of all relevant documents retrieved.
Precision
Proportion of retrieved material actually relevant.

36. Precision vs. Recall
All docs
Retrieved
Relevant

37. Case Study

38. Emerging Applications
Automatic evidence recommendations from the medical record.
Medical record summarization.
Image Search in Publications.
Identifying Subsets of Patients from the Medical Record.

39. In Summary

40. Summary Defined Information Retrieval. Explored Physician Questions.
Showed examples of retrieval.

41. A neat trick