Simplifying Semantic Solutions
for Biomedical Applications:
San Diego Semantic Web Meet-up
Eric Little, PhD
Chief Knowledg...
Current Biomedical Ontologies Exist in 3
Modes
Metaphysically-based category
systems that can properly classify
biomedical...
The Positive Impacts of
Ontologies in Biomedical
Domains
Promoting Fundamental Change
Decisions based upon
Medical Informatics
Decisions based upon
Claims and Codes
ProactiveReact...
Ontology and Advanced Business
Intelligence
• CTG provides a combination of innovative technologies that deliver
unique me...
Integrating Expertise
Medical Experts
Researchers
Ontology
Collective
Knowledge
Ontology
Collective
Knowledge
Technologist...
Ontologies should provide “actionable intelligence” across
an entire organization
• Knowledge Specification – Provide form...
Capturing Medical Knowledge in the Ontology
(Knowledge of Ranges  Rules)
• The normal ranges for hemoglobin depend on the...
Merging Independent Domain Ontologies
• Ontologies provide:
– Relations between
blood chemistry,
medical conditions and
sy...
Diagnosis
SNOMED-CT ICD9
Ontology Storyboard for Medical Informatics
Physical Test Results
LOINC
Medical Conditions
SNOMED...
Objectives of Ontology-based Biomedical Systems
• Find new insights
• Provide new knowledge and insights by running infere...
Technology Stack
RDF RDF SchemaEstablish Fact Base
XML XML Schema XSLTStandard Structure&Syntax
OWL–DL Ontology
Model Conc...
Layered Inference based upon
Medical Informatics
Medical Concepts & Facts
Medical Conditions
& Disease Risk Models
Disease...
Current Challenges for
Representing Biomedical
Information with Ontologies
Knowledge Management Technologies for
Drugs/Immunizations
• Utilizes
federated sub-
ontologies to:
– Classify types of
dis...
Advanced Research Capabilities
• Information can be manually searched or automatically linked (via
RSS feeds)
• Allows res...
Graphical Visualizations of Important
Relationships in the Data
• Different data sets can be graphically related to one an...
Building a Disease Registry
• Proactive Identification of a Disease Risk Constituency allows a Payer to take action
(inter...
Inferring Patient Complexity, Conditions, Disease & Co
Morbidity
• The Medical Management ontology utilizes an inference e...
Rapid Researching of Disease Management Related Issues
• Allows one to quickly identify and apply
the most available resea...
SELECT ?lotno ?plrcount ?scocount ?sumcount ?factor
WHERE {
{SELECT ?lotno (COUNT(*) AS ?plrcount)
WHERE {
?plr a PLR:Pall...
Complexities of Configuring Reasoning
(e.g., SPARQL Motion)
So…Complexities Abound
Enter Simplified Semantic Solutions
(e.g., MedMaP)
How Knowledge is Delivered – Medical
Management Portal (MedMaP)
• Support access to multiple analytical tools (portlets)
•...
Graphical Visualizations of Important Relationships in the
Data
• Different data sets can be
graphically related to one
an...
Cohort Populations: Identifying Critical Relationships
within the Ontology
• Information from disparate
sources can be rap...
Inference Visualized for Medical Informatics Portal
Therapeutic Optimization for Disease Management
• Example: Effective Treatment
Management
– Prescribing large doses of epo...
Therapeutic Optimization for Disease Management
• Example: Potentially Ineffective
Treatment Management
– Prescribing Larg...
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Simplifying semantics for biomedical applications

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  • If you cannot take action with the intelligence what is the point – this is usually not science for the sake of science
  • If you cannot take action with the intelligence what is the point – this is usually not science for the sake of science
  • Simplifying semantics for biomedical applications

    1. 1. Simplifying Semantic Solutions for Biomedical Applications: San Diego Semantic Web Meet-up Eric Little, PhD Chief Knowledge Engineer Eric.Little@CTG.com
    2. 2. Current Biomedical Ontologies Exist in 3 Modes Metaphysically-based category systems that can properly classify biomedical entities & relations. Improperly structured coding systems (ICD-9/10, LOINC, EHRs, etc) where little care has been taken in providing the proper formal standardization. Ontology development tools used for implementing models and their accompanying GUIs.
    3. 3. The Positive Impacts of Ontologies in Biomedical Domains
    4. 4. Promoting Fundamental Change Decisions based upon Medical Informatics Decisions based upon Claims and Codes ProactiveReactive Rich Internet Experience & Self Service Static or limited delivery Current Technology ApproachesCurrent Technology Approaches New Technology ApproachesNew Technology Approaches Example: Current systems rely on things like ICD-9 and CPT codes - so patients already possess the chronic condition (past the threshold for a disease) - it is hard to objectively judge treatment efficacy The need is to proactively recognize the complexities that lead to chronic disease and use objective measures to judge appropriate courses of action.
    5. 5. Ontology and Advanced Business Intelligence • CTG provides a combination of innovative technologies that deliver unique medical insights into: – New Research Areas • Reuse & repurposing of knowledge – Disease Registries – Interventions – Physician Feedback – Outcomes Analysis – Treatment Efficacy – Provider Efficacy – Cost Effectiveness – Waste Reduction OntologyReasoning Engines Semantic Web
    6. 6. Integrating Expertise Medical Experts Researchers Ontology Collective Knowledge Ontology Collective Knowledge Technologists Automated & Online Resources Medical Management & Claims Knowledge Base Formal Structuring of Information Improved understanding of data by using ontological categories – allows for action to be taken
    7. 7. Ontologies should provide “actionable intelligence” across an entire organization • Knowledge Specification – Provide formal definitions of all items/relations in a domain. – E.g., A substance such as Alpha-fetoprotein exists in high levels in healthy fetuses and infants and plays an important role in their health and development. High levels in adults is an indicator of liver cancer. Thus the same protein can be positive or negative based on age. • Knowledge Elicitation – Provide search/query capabilities to better find and utilize pertinent information. – E.g., What are all the known compounds that can act as an SSRI drug? Which treatments have proven the most effective for a particular disease type? • Knowledge Transfer – Provide improved communication between 1) human-to-machine, 2) human-to-human, 3) machine-to-machine. – E.g., Improves understanding of complex things like disease states and provides added insights – requires linking disparate knowledge from different experts. Traditional technologies can prove difficult to provide these kinds of capabilities. Ontology-based systems can help to integrate people through the use of a common semantic framework.
    8. 8. Capturing Medical Knowledge in the Ontology (Knowledge of Ranges  Rules) • The normal ranges for hemoglobin depend on the age and, beginning in adolescence, the gender of the person. The normal ranges are: • Newborns: 17-22 gm/dl • One (1) week of age: 15-20 gm/dl • One (1) month of age: 11-15gm/dl • Children: 11-13 gm/dl • Adult males: 14-18 gm/dl • Adult women: 12-16 gm/dl • Men after middle age: 12.4-14.9 gm/dl • Women after middle age: 11.7-13.8 gm/dl
    9. 9. Merging Independent Domain Ontologies • Ontologies provide: – Relations between blood chemistry, medical conditions and symptoms – Relations between diseases, diagnoses and treatments – Knowledge integration across a broad spectrum of applications – A common medical semantics – gives meaning to codes and integrates the information contained in those codes Integrate Integrate Integrate Diagnosis Ontology Disease Ontology Allows inference across various domains Cell Ontology LOINC Ontology
    10. 10. Diagnosis SNOMED-CT ICD9 Ontology Storyboard for Medical Informatics Physical Test Results LOINC Medical Conditions SNOMED-CT Medical Claims ICD9 - CPT EHR Disease Risk Registry SNOMED-CT Disease Registry SNOMED-CT Treatment SNOMED-CT ,CPT Co-morbidities Disease Model Example of Utilizing MedMaP for Liver Cancer Screening LOINC CODE 1834-1 AFP Ser-mCnc ICD-9 155 Elevated AFP Age Ontology: Adult Male Disease Risk Ontology: Hepatocellular Carcinoma (HCC) ICD-9 155 Foundational Model Human Anatomy Ontology: LIVER – Yolk Sac Cell Ontology: Liver Cells Dendrite Cell Ontology: Immune Cells Patient Cohorting Gene Ontology: Chromosome 4 Gene ID - 174 CPT 47135: Liver Transplant Pharmaceutical Treatments Drug Bank Ontology: Sorafenib Hepatitis B Hepatitis C Patient Age, Sex, etc
    11. 11. Objectives of Ontology-based Biomedical Systems • Find new insights • Provide new knowledge and insights by running inference engines (the system itself should multiply the knowledge) • Establish and apply business rules which entail a higher level of reasoning • Capture SME expertise and deliver globally • Establish a model that captures SME approach and science (Establish a collaborative community) • Insulate the common user from the complexity unless it is requested • Access to knowledge should not require IT intervention • Allow user to customize their experience to meet their specific needs and objectives • Portal to allow for customized experience • Multi-perspectivealism should allow multiple views or renderings of the same data • Also support static views to dictate or direct useable experience • Deliver intelligence in an intuitive easily consumable format • Ease of Use = High Adoption • Interactive and tactile feel tp allow the user to experience and interact with the ABI system • Rendered / skinned in format familiar to users surroundings • Utilize Common Semantics that are relevant to the user • Health Care Provider – Hypertensive (140 systolic / 90 diastolic) is more common to Patient as High Blood Pressure • Model must support synonyms • Educate • Allow users to gain insights to SME expertise, and reasoning results • News feeds, research, sparql end points, lexicons of relevant terms, video • Provide Evidence to support findings • Ability to drill into reasoning and scoring and expose business rules, raw data, formulas, and foundations of knowledge • Make intelligence “actionable” • Allow people to take action with the knowledge gained
    12. 12. Technology Stack RDF RDF SchemaEstablish Fact Base XML XML Schema XSLTStandard Structure&Syntax OWL–DL Ontology Model Concepts into Knowledge Base REASONER Provide Reasoning and Inference Rule Execution Take Action based on Knowledge IncreaseIntelligence
    13. 13. Layered Inference based upon Medical Informatics Medical Concepts & Facts Medical Conditions & Disease Risk Models Disease Registries & Recommend treatments Interventions & Outcomes & Efficacy IncreasesCapability Claims & Labs Establish Fact Base Standard Structure&Syntax Model Concepts into Knowledge Base Provide Reasoning and Inference Take Action based on Knowledge
    14. 14. Current Challenges for Representing Biomedical Information with Ontologies
    15. 15. Knowledge Management Technologies for Drugs/Immunizations • Utilizes federated sub- ontologies to: – Classify types of diseases treated – Classify active and inactive ingredients – Classify potential side effects -e.g., contraindications – Classify dosages – Classify chemical composition/for mula – Classify pathways Class Hierarchy Product of Interest (Pentacel Immunization) Graph of Items in The Ontology Web Search Capabilities On Any Item in Ontology (e.g., Pentacel)
    16. 16. Advanced Research Capabilities • Information can be manually searched or automatically linked (via RSS feeds) • Allows researchers to find the information they need very rapidly. • Information gathered through these inputs can be quickly ingested into the ontology – improves the knowledge base over time Specific Research Article Details of the Article On-line Resource
    17. 17. Graphical Visualizations of Important Relationships in the Data • Different data sets can be graphically related to one another. • Allows for rapid insights into new kinds of relationships. • Can be used to represent individual records (e.g., indiv. patient data) versus group data (e.g., groups of patients classified into categories – morbidly obese, clinically obese, etc. Shows Overlap of Different Categories
    18. 18. Building a Disease Registry • Proactive Identification of a Disease Risk Constituency allows a Payer to take action (intervene or educate) and perform preventive maintenance. • Abilities for Predictive Modeling of Disease Risk, etc.
    19. 19. Inferring Patient Complexity, Conditions, Disease & Co Morbidity • The Medical Management ontology utilizes an inference engine auto classifies based upon industry accepted reference ranges and intelligent rules to determine complexity factors such as conditions, disease risk, etc. • Blue Lines in the diagram below identify knowledge learned (inferred) from the inference engine.
    20. 20. Rapid Researching of Disease Management Related Issues • Allows one to quickly identify and apply the most available research on a given topic. • Information can be manually searched • Information can be automatically linked to the ontology (RSS feeds). • Allows the knowledge base of diseases (and related symptoms/blood chemistry readings) to continuously grow over time. • Knowledge of disease becomes an asset over time.
    21. 21. SELECT ?lotno ?plrcount ?scocount ?sumcount ?factor WHERE { {SELECT ?lotno (COUNT(*) AS ?plrcount) WHERE { ?plr a PLR:Pallet_Reading . ?plr PLR:lot_no ?lotno . } GROUP BY ?lotno } OPTIONAL { {SELECT ?lotno ?scocount WHERE { ?sco a SCO:Lot_Score . ?sco PLR:lot_no ?lotno . ?sco SCO:hasPalletCount ?scocount } } } OPTIONAL { {SELECT ?lotno (SUM (?count) AS ?sumcount) WHERE { ?s a SUM:Lot_Summary . ?s PLR:lot_no ?lotno . ?s SUM:hasSummaryCount ?count } GROUP BY ?lotno } } LET (?factor := ?sumcount / ?plrcount) } ORDER BY ?lotno CONSTRUCT { ?uri a SUM:Hourly_Summary . ?uri PLR:lot_no ?lotno . ?uri PLR:captureevtdate ?date . ?uri PLR:hasprocessinghour ?hr . ?uri CLS:hasClassifyGroup ?classifyGroup . ?uri CLS:hasClassifyProperty ?classifyProp . ?uri CLS:hasClassifyCategory ?classifyCategory . ?uri SUM:hasSummaryCount ?LotHrCount . } WHERE { { SELECT ?lotno ?date ?hr ?classifyGroup ?classifyProp ? classifyCategory (SUM(?Count) AS ?LotHrCount) WHERE { ?sum a SUM:Hourly_Summary . ?sum PLR:lot_no ?lotno . ?sum PLR:captureevtdate ?date . ?sum PLR:hasprocessinghour ?hr . ?sum CLS:hasClassifyGroup ?classifyGroup . ?sum CLS:hasClassifyProperty ?classifyProp . ?sum CLS:hasClassifyCategory ?classifyCategory . ?sum SUM:hasSummaryCount ?Count . } GROUP BY ?lotno ?date ?hr ?classifyGroup ? classifyProp ?classifyCategory } . LET (?uuid := smf:generateUUID()) . LET (?uri := smf:buildURI("<http://www.ctg.com/SUM#{?lotno}_{? date}_{?hr}_{?uuid}>")) . } Examples of Complex SPARQL Queries
    22. 22. Complexities of Configuring Reasoning (e.g., SPARQL Motion)
    23. 23. So…Complexities Abound
    24. 24. Enter Simplified Semantic Solutions (e.g., MedMaP)
    25. 25. How Knowledge is Delivered – Medical Management Portal (MedMaP) • Support access to multiple analytical tools (portlets) • Users customize their experience to meet their specific objectives • Support multiple perspectives (Different views for different people) • Allow users to self help – self serve • Educate • Allow users to gain insights to SME expertise, and reasoning results • News feeds, research, sparql end points, lexicons of relevant terms, video • Support static views to dictate or direct the most efficient experience
    26. 26. Graphical Visualizations of Important Relationships in the Data • Different data sets can be graphically related to one another. • Allows for rapid insights into new kinds of relationships. • Can be used to represent individual records (e.g., indiv. patient data) versus group data (e.g., groups of patients classified into categories – morbidly obese, clinically obese, etc. Yellow Balls Represent Individual Patients Purple Balls Represent Individual Patients With Added Attribute of HYPERGLYCEMIA
    27. 27. Cohort Populations: Identifying Critical Relationships within the Ontology • Information from disparate sources can be rapidly compared using industry standard classifications. – Can relate information across different medical areas. • The system can be used to link blood chemistry values to one another (e.g., glucose readings & BUN scores). • It can link those values to other items (e.g., patient attributes – gender, Body Mass Index (BMI), etc.)
    28. 28. Inference Visualized for Medical Informatics Portal
    29. 29. Therapeutic Optimization for Disease Management • Example: Effective Treatment Management – Prescribing large doses of epoetin alpha for low levels of hemoglobin (Anemia) in CKD patients. – Outcomes analysis can be used to determine whether this is an appropriate course of action.
    30. 30. Therapeutic Optimization for Disease Management • Example: Potentially Ineffective Treatment Management – Prescribing Large Doses of Epoetin for normal Hemoglobin. – Can be used to identify when a treatment is not warranted based on empirical evidence (outcomes). – Resulting Question: Why are large and costly doses of epoetin alpha being given to patients with normal hemoglobin levels?

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