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Information Retrieval and its Application in Biomedicine. Sept 4 Introduction. Hong Yu 1,2 , PhD Susan McRoy 1 , PhD 1 Department of Computer Science 2 Department of Health Sciences University of Wisconsin-Milwaukee. What is Information Retrieval?.

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information retrieval and its application in biomedicine
Information Retrieval and its Application in Biomedicine

Sept 4 Introduction

Hong Yu1,2, PhD

Susan McRoy1, PhD

1Department of Computer Science

2Department of Health Sciences

University of Wisconsin-Milwaukee

what is information retrieval
What is Information Retrieval?
  • The field concerned with the acquisition, organization, and searching of knowledge-based information. (Hersh, 2003)
information
Information
  • World Wide Web
  • Company Documentations
  • Drug Descriptions
  • Medical Records
  • Books
  • Everything that is text, image, video, and sound, and that can be transformed digitally
information in biomedicine
Information in Biomedicine
  • Literature (over 17 million publications)
  • WWW
  • Electronic medical records
  • Genomics data
    • DNA sequences, etc.
  • Knowledge representation
    • Gene Ontology
  • Company databases
    • Micromedex drug database
ir in biomedicine
IR in Biomedicine
  • Index Medicus (Billings 1879)
  • MEDLARS (NLM 1966)
  • SAPHIRE (Hersh 1990)
  • PubMed (NLM 1996)
  • Arrowsmith (Smalheiser 1998)
  • BioText (Hearst 2003)
  • BioMedQA (Yu 2006)
electronic and open publishing
Electronic and Open Publishing
  • Internet and Web have a profound impact on the publishing of knowledge-based information
  • Most of literature can be electronically available
  • Open-access
    • The Bethesda Statement on Open Access Publishing (http://www.earlham.edu/~peters/fos/bethesda.htm) (April 11, 2003)
    • The Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities (http://www.zim.mpg.de/openaccess-berlin/berlindeclaration.html). (2003)
    • PubMedCentra (NLM 2004)
quality of information
Quality of Information
  • A lack of quality control
    • Anyone can publish online
    • A wealthy of studies concluded that Web has a poor quality for healthcare information
  • Readability
    • Hard to read
information needs and seeking
Information Needs and Seeking
  • Unrecognized needs
    • Clinicians unaware of information needs or knowledge deficit
  • Recognized needs
    • Clinicians aware of needs but may or may not pursue them
  • Pursued needs
    • Information seeking occurs but may or may not be successful
  • Satisfied needs
    • Information seeking successful
what you will learn
What You Will Learn
  • IR algorithms
    • Indexing
    • Query and Retrieval
    • Evaluation
    • Text Classification
    • XML retrieval
    • Web retrieval
what you will learn cont
What You Will Learn (Cont.)
  • Open-Source IR tools
    • What open-source IR tools are available
      • Indexing/retrieval
      • Part-of-speech and syntactic parsing
      • Semantic parsing
      • Discourse relations
      • Machine-learning classifiers
  • How to use the tools?
what you will learn cont1
What You Will Learn (Cont.)
  • State of the art IR systems
    • Baruch 1965 [BLIMP http://blimp.cs.queensu.ca/index.html]
    • SAPHIRE (Hersh 1990)
      • Retrieval
    • MedLEE (Friedman 1994)
      • Extraction
    • PubMed (NLM 1997)
    • ARROSMITH Systems (Smalheiser 1998)
      • Hidden Relation Discovery Tool
    • GENIES (Friedman 2001)
      • Extraction
slide14
BioNLP Systems
  • BioText (Hearst 2003http://biotext.berkeley.edu/)
    • Retrieval+Categorization
  • GeneWays (Rzhetsky 2004 http://geneways.genomecenter.columbia.edu/)
    • Extraction+Visualization
  • TextPresso (Muller 2004http://www.textpresso.org/)
    • Retrieval+Extraction
  • iHOP (Hoffman and Valencia 2005http://www.ihop-net.org/UniPub/iHOP/)
    • Retrieval
  • BioMedQA (Yu 2006 http://monkey.ims.uwm.edu/MedQA)
    • Question Answering
beyond text image and video
Beyond text: Image and Video
  • Image classification
    • Finding concepts in captions and annotations
    • Machine learning on textual & visual features
    • Determining salient features in text and image separately and merging the results
  • Extracting text from image
    • Understanding and correcting OCR (handwriting, equations)
    • Finding text in images
  • Finding document text related to illustrations
  • Video retrieval
resources
Resources
  • Annotated collections (GENIA, Medstract, Yapex …)
  • Ontologies, tools, knowledge bases …
  • Publications, Conferences, Evaluations …
  • Centres and web portals
what we provide
What We Provide
  • Textbook
    • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze. Introduction to Information Retrieval. Cambridge University Press, 2007
      • http://www-csli.stanford.edu/~schuetze/information-retrieval-book.html
  • Office hour:
    • Tuesdays, 3-4 pm EMS 710 and by appointment
    • Hong Yu, 414-229-3344
    • Susan McRoy, 414-229-6695
what we expect
What We Expect
  • Undergraduate:
    • 30% Homework, 35% Midterm exam, 35% Final exam or project
  • Graduate:
    • 20% Midterm exam, 40% Homework, 40% Project: The project may be done individually or in a team of 2-3 people. The final project will include a software system, a 2-3 page written project report, and an oral presentation. The report should describe the problem, the approach, and evaluation and should cite related work where appropriate.
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