A Biomedical Information Retrieval System based on Clustering for Mobile Devices

Manuel de la VillaManuel Millán

Alejandro MuñozManuel J. Maña

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This work has been partially funded by the Spanish Ministry of Science and Innovation and the European Union from the ERDF (TIN2009-14057-C03-03)

BioSEPLN10. Workshop on Language Technology applied to biomedical and health documents. Valencia, 6th september 2010

A Biomedical Information Retrieval System based on Clustering for Mobile DevicesMain index

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A Biomedical Information Retrieval System based on Clustering for Mobile Devices

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Main index



Medical staff mobility “Hospitals staff might be distributed in space or time and their

information needs are highly dependent on contextual conditions.“ (Muñoz et al, 2003)

“…PDA use by health professionals shows an evolution in the use ranging from 30% in 2000 to 60% in 2006” (Garrity and El Emam, 2006)

the available resources accessible from the PDA at the bedside provided response to 86% of clinical questions, most of them (88.9% - 97.7%) during the rounds of visits (Hauser et al. 2007)

Introduction

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Motivation



Evidence-based medicine “Evidence based medicine is the conscientious, explicit, and

judicious use of current best evidence in making decisions about the care of individual patients. The practice of evidence based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic Research. “(Sackett et Al., 1996)

IntroductionMotivation

Is useful an IRS to locate the best available external clinical evidence?



Wi-fi

Efficient access

PDA

Best evidence

Point –of-care

Mobility

Information overload

CLUstering on Mobile MEdical Devices

IntroductionMotivation

YAIRS? Yet Another IRS? Novelty?

Post-retrieval clustering, orientation to biomedical documentsand mobile devices


A Biomedical Information Retrieval System based on Clustering for Mobile DevicesIntroduction

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Post-retrieval clustering is a known tecnique that improve theorganization of the search results and facilitate navigation betweenthem.

Previous experiences on Search Engines using clustering:Univesity Carnegie-Mellon -> Vivisimo -> Clusty

Interesting? No? And if I tell you that on May Yippy has paid $5.5M for Clusty…?

Post-retrieval clustering?


A Biomedical Information Retrieval System based on Clustering for Mobile DevicesIntroduction

Post-retrieval clustering on Biomedicine?



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Index



Queryrepresentation

Analysis

Textprocessing

Documentsrepresentation

DocumentsRepository

RelevantDocuments

Similaritycalculation

Informationneeded

Information Retrieval System

Given a set of documents and an information need, the goal of IR is to obtain thedocuments relevant to that need, sort by any criteria and show them to the user.

1. Indexing

2. Query

3. Searching

4. Evaluation

5. Ranking

Search and Information RetrievalA tipical Schema



Document sources: Biomed Central (web crawling in progress)

Text Processing: lowercasing, stemming, stop-words ,…

Search and Information RetrievalOur implementation

Lucene for indexing…


A Biomedical Information Retrieval System based on Clustering for Mobile DevicesSearch and Information Retrieval

Our implementation (and II)

… and Lucene for searching



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Index



Clustering

The post-processing clustering is to associate, according to their similarity, a set of documents retrieved from a query in different subsets

ClusteringOur implementation

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Clustering algorithm:

Simple-K-Means vs Expectation Maximization

Algorithms

Querys (Documents)Simple-K-means EM

Ligaments (10) 1 2

Cancer Skin (25) 4 12

Cancer (46) 5 26

Disease (62) 8 57

Time it takes to perform the grouping in seconds

K? It depends on the number of documents retrieved.

ClusteringWhy Simple-K-Means?

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Index



Visualization on Mobile Devices

Restrictions:

Compatibility problems Screen size Multiples windows lack Limited navigation Limited memory size Javascript, cookies Accesibility etc.

Solutions:

World Wide Web Consortium (W3C): Mobile Web Initiative. "The Mobile Web Initiative’s goal is to make browsing the Web from mobile devices a reality, to improve Web content production and access for mobile users” (Tim Berners-

Lee, W3C Director)

Mobile Web Best Practice 1.0.

Visualization on Mobile DevicesTypical problems

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One webmaking, as far as is reasonable, the same information and services available to users irrespective of the device they are using

Trust in web standardsHTML compatible with different browsers, Use Stylesheet, Content in blocks (<DIV>).

Avoid known risksNo pop-ups, No frames, No tables

Controlling limitationsNo scripting, Standards fonts, Use of color

Optimized navigationMinimal navigation at the top of the pageAvoid lengthy URI’s

Probe images and coloursReduced image size and resolution, good contrast

Do it smallSmall pages, only one-direction (vertical) scrolling, easy entry forms (reduced keytyping)

Limited use of networkNo external links (images…), no download

Think in usersSimple language, relevant and limited content, error messages

And many more…!!!18

Visualization on Mobile DevicesSome best practices



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Visualization on Mobile DevicesHigh resolution interface



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Visualization on Mobile DevicesLow resolution interface



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Visualization on Mobile DevicesChecking ClusterMed



Our proposal obteins at his first version a 76%

Visualization on Mobile DevicesChecking our proposal



Cancer skin

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Visualization on Mobile DevicesOur interface



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Conclusions

Objectives Fulfilled

It works!!!

First Milestone

A working prototype of an Information retrieval

System adapted for Medical Devices based on Post-

retrieval clustering

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Conclusions and future workConclusions



A lot of…

Focus on using medical ontologies like UMLS

Metathesarus for:

Improve the clustering quality

Enhance the labelling of the groups

Visual help, graph with concepts/cluster relationship

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… and future work:

Conclusions and future workFuture work



… and future work (and II):

Monodocument Summarization

Freebase Ajax connection for search support

Bilingual (Spanish-English documents)

Put into production (crawling and indexing…) any sponsor at the hall???

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Conclusions and future workFuture work (and II)



Manuel de la VillaManuel J. Maña{manuel.villa, manuel.mana}@dti.uhu.es

Manuel MillánAlejandro Muñoz{manuel.millan, alejandro.munoz}@alu.uhu.es

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This work has been partially funded by the Spanish Ministry of Science and Innovation and the European Union from the ERDF (TIN2009-14057-C03-03)