Information retrieval and its application in biomedicine
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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)


Speed up communication

Speed Up Communication


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


Evidence based medicine

Evidence-Based Medicine


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


Information retrieval and its application in biomedicine

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


Advanced nlp applications

Advanced NLP applications


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


Beyond extraction experimental tools

Beyond Extraction: Experimental Tools


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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