Semantic empowerment of Health Care and Life Science Applications

Transcription

1 Wright State University CORE Scholar Kno.e.sis Publications The Ohio Center of Excellence in Knowledge- Enabled Computing (Kno.e.sis) Semantic empowerment of Health Care and Life Science Applications Amit P. Sheth Wright State University - Main Campus, amit.sheth@wright.edu Follow this and additional works at: Part of the Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, and the Science and Technology Studies Commons Repository Citation Sheth, A. P. (2006). Semantic empowerment of Health Care and Life Science Applications.. This Presentation is brought to you for free and open access by the The Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis) at CORE Scholar. It has been accepted for inclusion in Kno.e.sis Publications by an authorized administrator of CORE Scholar. For more information, please contact corescholar@

2 Semantic empowerment of Health Care and Life Science Applications WWW 2006 W3C Track, May Amit Sheth LSDIS Lab & Semagix University of Georgia Joint work with Athens Heart Center, and CCRC, UGA

3 Part I: A Healthcare Application Active Semantic Electronic Medical 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 2

4 Active Semantic Document A document (typically in XML) with Lexical and Semantic annotations (tied to ontologies) 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 3

5 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 4

6 One of 3 ontologies used (part of drug ontology) Dosage Form Intake Route Formulary Indication has_formulary has_indication Drug has_interaction Interaction has_type has_class reacts_with Type MonographClass Non-Drug Reactant Physical Condition CPNUMGrp Allergy Pregnancy Generic BrandName Local, licensed and public (Snomed) sources to populated ontologies 5

7 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 6

8 Exploration of the neighborhood of the drug Tasmar Formulary_1498 Tasmar generic/brandname Telcapone belongsto belongsto interacts_with CPNUMGroup_2119 interacts_with classification CPNUMGroup_2118 CPNUMGroup_20 6 Neurological Agents COMT Inhibitors 7

9 Active Semantic Doc with 3 Ontologies 8

10 Explore neighborhood for drug Tasmar Explore: Drug Tasmar 9

11 Explore neighborhood for drug Tasmar classification classification belongs to group classification interaction belongs to group brand / generic Semantic browsing and querying-- perform decision support (how many patients are using this class of drug, ) 10

12 Software Architecture 11

13 12 ROI

14 Athens Heart Center Practice Growth Appointments (excluding cancelled/rescheduled but including noshow cases) appts jan feb mar apr may jun month jul aug sep oct nov dec Increased efficiency demonstrated as more encounters supported without increasing clinical staff

15 Chart Completion before the preliminary deployment of the ASMER Charts Same Day Back Log Jan 04 Mar 04 May 04 Jul 04 Sept 04 Nov 04 Jan 05 Mar 05 May 05 Jul 05 Month/Year 14

16 Chart Completion after the preliminary deployment of the ASMER Charts Sept 05 Nov 05 Jan 06 Mar 06 Same Day Back Log Month/Year 15

17 16 Applying Semantic Technologies to the Glycoproteomics Domain

18 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 ontologies, 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 17

19 GlycO 18

20 GlycO ontology 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 19

21 GlycoTree β-d-glcpnac -(1-2)- α-d-manp -(1-6)+ β-d-manp -(1-4)- β-d-glcpnac -(1-4)- β-d-glcpnac β-d-glcpnac -(1-4)- α-d-manp -(1-3)+ β-d-glcpnac -(1-2)+ 20 N. Takahashi and K. Kato, Trends in Glycosciences and Glycotechnology, 15:

22 Ontology population workflow YES: next Instance Semagix Freedom knowledge extractor Instance Data Already in KB? Has CarbBank ID? NO IUPAC to LINUCS NO YES Insert into KB Compare to Knowledge Base LINUCS to GLYDE 21

23 GlycO population YES: next Instance Semagix Freedom knowledge extractor Already in KB? NO Instance Data Has CarbBank ID? YES [][Asn]{[(4+1)][b-D-GlcpNAc] {[(4+1)][b-D-GlcpNAc] {[(4+1)][b-D-Manp] {[(3+1)][a-D-Manp] IUPAC to NO {[(2+1)][b-D-GlcpNAc] {}[(4+1)][b-D-GlcpNAc] LINUCS {}}[(6+1)][a-D-Manp] {[(2+1)][b-D-GlcpNAc]{}}}}}} 22 Insert into KB Compare to Knowledge Base LINUCS to GLYDE

24 Ontology Population Workflow 23 Already in KB? NO Insert into KB Semagix Freedom knowledge extractor <Glycan> <aglycon name="asn"/> YES: <residue link="4" next anomeric_carbon="1" Instance anomer="b" chirality="d" monosaccharide="glcnac"> <residue link="4" anomeric_carbon="1" anomer="b" chirality="d" monosaccharide="glcnac"> <residue link="4" anomeric_carbon="1" anomer="b" Instance chirality="d" monosaccharide="man" > <residue link="3" anomeric_carbon="1" anomer="a" Data 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" NO monosaccharide="man" > Has CarbBank ID? YES Compare to Knowledge Base IUPAC to LINUCS <residue link="2" anomeric_carbon="1" anomer="b" chirality="d" monosaccharide="glcnac"> </residue> </residue> </residue> </residue> </residue> </Glycan> LINUCS to GLYDE

25 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. 24

26 Zooming in a little Reaction R05987 catalyzed by enzyme adds_glycosyl_residue N-glycan_b-D-GlcpNAc_13 The N-Glycan with KEGG ID is the substrate to the reaction R05987, which is catalyzed by an enzyme of the class EC The product of this reaction is the Glycan with KEGG ID

27 N-Glycosylation Process (NGP) Cell Culture Glycoprotein Fraction Glycopeptides Fraction 1 n Glycopeptides Fraction n Peptide Fraction n*m Peptide Fraction extract proteolysis Separation technique I PNGase Separation technique II Mass spectrometry Glycopeptide identification N-dimensional array and 26 quantification Signal integration ms data ms peaklist Data reduction binning Data correlation ms/ms data ms/ms peaklist Peptide list Data reduction Peptide identification

28 Semantic Annotation of MS Data parent ion m/z fragment ion m/z parent ion charge parent ion abundance fragment ion abundance ms/ms peaklist data 27

29 Semantic annotation of Scientific Data <ms/ms_peak_list> <parameter instrument= micromass_qtof_2_quadropole_time_of_flight_mass_s pectrometer mode = ms/ms /> <parent_ion_mass> </parent_ion_mass> <total_abundance> </total_abundance> <z>2</z> <mass_spec_peak m/z = abundance = /> <mass_spec_peak m/z = abundance = /> <mass_spec_peak m/z = abundance = /> <mass_spec_peak m/z = abundance = /> <mass_spec_peak m/z = abundance = /> <mass_spec_peak m/z = abundance = /> <mass_spec_peak m/z = abundance = /> <mass_spec_peak m/z = abundance = /> <ms/ms_peak_list> Annotated ms/ms peaklist data 28

30 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 ontologies at the LSDIS lab: WWW2006 paper 29