Semantic empowerment of Health Care and Life Science Applications - - PowerPoint PPT Presentation

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Semantic empowerment of Health Care and Life Science Applications - - PowerPoint PPT Presentation

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Semantic empowerment of Health Care and Life Science Applications WWW 2006 W3C Track, May 26 2006 Amit Sheth LSDI S Lab & Sem agix University of Georgia Joint work with Athens Heart Center, and CCRC, UGA Part I: A

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Semantic empowerment of Health Care and Life Science Applications

WWW 2006 W3C Track, May 26 2006 Amit Sheth

LSDI S Lab & Sem agix University of Georgia

Joint work with Athens Heart Center, and CCRC, UGA

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Part I: A Healthcare Application Active Semantic Electronic Medical Record @ Athens Heart Center (use Firefox)

(deployed since Dec 2005) Collaboration between LSDIS & Athens Heart Center (Dr. Agrawal, Dr. Wingate For on line demo: Google: Active Semantic Documents

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Active Semantic Document

A document (typically in XML) with

  • Lexical and Semantic annotations (tied to
  • ntologies)
  • Active/ Actionable information (rules over

semantic annotations) Application: Active Semantic EMR for Cardiology Practice

  • EMRs in XML
  • 3 ontologies (OWL), populated
  • RDQL-> SPARQL, Rules
  • Services, Web 2.0
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Active Semantic Electronic Medical Record

Demonstrates use of Semantic Web technologies to

  • reduce medical errors and patient safety

– accurate completion of patient charts (by checking drug interactions and allergy, coding of impression,… )

  • improve physician efficiency, extreme user

friendliness, decision support

– single window for all work; template driven sentences, auto-complete, contextual info., exploration

  • improve patient satisfaction in medical practice

– Formulary check

  • improve billing due to more accurate coding and

adherence to medical guidelines

– Prevent errors and incomplete information that insurance can use to withhold payment

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One of 3 ontologies used (part of drug

  • ntology)

Drug Generic Interaction Formulary

Physical Condition

BrandName Indication Pregnancy has_interaction

Non-Drug Reactant

has_indication has_formulary

Dosage Form

Intake Route

MonographClass

Type CPNUMGrp Allergy has_type has_class reacts_with

Local, licensed and public (Snomed) sources to populated ontologies

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Example Rules

  • drug-drug interaction check,
  • drug formulary check (e.g., whether the drug is

covered by the insurance company of the patient, and if not what the alternative drugs in the same class of drug are),

  • drug dosage range check,
  • drug-allergy interaction check,
  • ICD-9 annotations choice for the physician to

validate and choose the best possible code for the treatment type, and

  • preferred drug recommendation based on drug

and patient insurance information

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Exploration of the neighborhood of the drug Tasmar

Tasmar Telcapone Formulary_1498 generic/brandname CPNUMGroup_2119 belongsTo belongsTo interacts_with CPNUMGroup_2118 interacts_with CPNUMGroup_20 6 classification Neurological Agents COMT Inhibitors

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Active Semantic Doc with 3 Ontologies

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Explore neighborhood for drug Tasmar

Explore: Drug Tasmar

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Explore neighborhood for drug Tasmar

belongs to group belongs to group brand / generic classification classification classification interaction

Semantic browsing and querying-- perform decision support (how many patients are using this class of drug, …)

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Software Architecture

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ROI

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Athens Heart Center Practice Growth

Appointments (excluding cancelled/rescheduled but including noshow cases)

200 400 600 800 1000 1200 1400 1600 j a n f e b m a r a p r m a y j u n j u l a u g s e p

  • c

t n

  • v

d e c month appts 2003 2004 2005 2006

Increased efficiency demonstrated as more encounters supported without increasing clinical staff

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Chart Completion before the preliminary deployment of the ASMER

100 200 300 400 500 600 J a n 4 M a r 4 M a y 4 J u l 4 S e p t 4 N

  • v

4 J a n 5 M a r 5 M a y 5 J u l 5 Month/Year Charts Same Day Back Log

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Chart Completion after the preliminary deployment of the ASMER

100 200 300 400 500 600 700 Sept 05 Nov 05 Jan 06 Mar 06 Month/Year Charts Same Day Back Log

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Applying Semantic Technologies to the Glycoproteomics Domain

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Quick take on bioinformatics ontologies and their use

  • GlycO and ProPreO - among the largest populated ontologies

in life sciences

  • Interesting aspects of structuring and populating these
  • ntologies, and their use
  • GlycO

– a comprehensive domain ontology; it uses simple ‘canonical’ entities to build complex structures thereby avoids redundancy → ensures maintainability and scalability – Web process for entity disambiguation and common representational format → populated ontology from disparate data sources – Ability to display biological pathways

  • ProPreO is a comprehensive ontology for data and process

provenance in glycoproteomics

  • Use in annotating experimental data, high throughput

workflow

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GlycO

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  • Challenge – model hundreds of thousands of

complex carbohydrate entities

  • But, the differences between the entities are

small (E.g. just one component)

  • How to model all the concepts but preclude

redundancy → ensure maintainability, scalability

GlycO ontology

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  • N. Takahashi and K. Kato, Trends in Glycosciences

and Glycotechnology, 15: 235-251

β-D-GlcpNAc β-D-GlcpNAc β-D-Manp-(1-4)-

  • (1-4)-

α-D-Manp -(1-6)+ β-D-GlcpNAc-(1-2)- α-D-Manp -(1-3)+ β-D-GlcpNAc-(1-4)- β-D-GlcpNAc-(1-2)+

GlycoTree

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Has CarbBank ID? IUPAC to LINUCS LINUCS to GLYDE Compare to Knowledge Base Already in KB? YES NO Semagix Freedom knowledge extractor Instance Data YES: next Instance Insert into KB NO

Ontology population workflow

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Has CarbBank ID? IUPAC to LINUCS LINUCS to GLYDE Compare to Knowledge Base Already in KB? YES NO Semagix Freedom knowledge extractor Instance Data YES: next Instance Insert into KB NO [][Asn]{[(4+1)][b-D-GlcpNAc] {[(4+1)][b-D-GlcpNAc] {[(4+1)][b-D-Manp] {[(3+1)][a-D-Manp] {[(2+1)][b-D-GlcpNAc] {}[(4+1)][b-D-GlcpNAc] {}}[(6+1)][a-D-Manp] {[(2+1)][b-D-GlcpNAc]{}}}}}}

GlycO population

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Has CarbBank ID? IUPAC to LINUCS LINUCS to GLYDE Compare to Knowledge Base Already in KB? YES NO Semagix Freedom knowledge extractor Instance Data YES: next Instance Insert into KB NO

<Gly <agly <residue link="4" anomeric_carb <residue link="4" anom </ </r </Gly can> con name="Asn"/>

  • n="1" anomer="b" chirality="D" monosaccharide="GlcNAc">

eric_carbon="1" anomer="b" chirality="D" monosaccharide="GlcNAc"> <residue link="4" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="Man" > <residue link="3" anomeric_carbon="1" anomer="a" chirality="D" monosaccharide="Man" > <residue link="2" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="GlcNAc" > </residue> <residue link="4" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="GlcNAc" > </residue> </residue> <residue link="6" anomeric_carbon="1" anomer="a" chirality="D" monosaccharide="Man" > <residue link="2" anomeric_carbon="1" anomer="b" chirality="D" monosaccharide="GlcNAc"> </residue> </residue> </residue> residue> esidue> can>

Ontology Population Workflow

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Pathway representation in GlycO

Pathways do not need to be explicitly defined in GlycO. The residue-, glycan-, enzyme- and reaction descriptions contain the knowledge necessary to infer pathways.

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Zooming in a little …

The N-Glycan with KEGG ID 00015 is the substrate to the reaction R05987, which is catalyzed by an enzyme of the class EC 2.4.1.145. The product of this reaction is the Glycan with KEGG ID 00020. Reaction R05987 catalyzed by enzyme 2.4.1.145 adds_glycosyl_residue N-glycan_b-D-GlcpNAc_13

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N N-

  • Glycosylation

Glycosylation Process Process (NGP NGP)

Cell Culture Glycoprotein Fraction Glycopeptides Fraction

extract Separation technique I

Glycopeptides Fraction

n*m n

Signal integration Data correlation

Peptide Fraction Peptide Fraction ms data ms/ms data ms peaklist ms/ms peaklist Peptide list N-dimensional array Glycopeptide identification and quantification

proteolysis Separation technique II PNGase Mass spectrometry Data reduction Data reduction Peptide identification binning

n 1

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830.9570 194.9604 2 580.2985 0.3592 688.3214 0.2526 779.4759 38.4939 784.3607 21.7736 1543.7476 1.3822 1544.7595 2.9977 1562.8113 37.4790 1660.7776 476.5043

parent ion m/z fragment ion m/z ms/ms peaklist data fragment ion abundance parent ion abundance parent ion charge

Semantic Annotation of MS Data

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Semantic annotation of Scientific Data Semantic annotation of Scientific Data

Annotated ms/ms peaklist data

<ms/ms_peak_list> <parameter instrument=“micromass_QTOF_2_quadropole_time_of_flight_mass_s pectrometer” mode = “ms/ms”/> <parent_ion_mass>830.9570</parent_ion_mass> <total_abundance>194.9604</total_abundance> <z>2</z> <mass_spec_peak m/z = 580.2985 abundance = 0.3592/> <mass_spec_peak m/z = 688.3214 abundance = 0.2526/> <mass_spec_peak m/z = 779.4759 abundance = 38.4939/> <mass_spec_peak m/z = 784.3607 abundance = 21.7736/> <mass_spec_peak m/z = 1543.7476 abundance = 1.3822/> <mass_spec_peak m/z = 1544.7595 abundance = 2.9977/> <mass_spec_peak m/z = 1562.8113 abundance = 37.4790/> <mass_spec_peak m/z = 1660.7776 abundance = 476.5043/> <ms/ms_peak_list>

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Summary, Observations, Conclusions

  • Deployed health care application that uses

SW technologies and W3C recommendations with some understanding of ROI

  • New methods for integration and

analysis/ discovery in biology driven by large populated ontologies

  • Projects, library and resources including
  • ntologies at the LSDIS lab:

http: / / lsdis.cs.uga.edu, WWW2006 paper