1. Harnessing Evidence Based Imaging An Architecture Based on Semantic Web Technology, Medical Ontologies, Reasoning Engines, and Pluggable Rule Sets Helen H. Chen, PhD 1, Glenn R. Potter 1, Paul G. Nagy, PhD 2 and Daniel L. Rubin, MD 3 1 Agfa Healthcare, 2 University of Maryland Medical Systems, 3 Stanford University

2. 2 RSNA2007, November 25-30 Motivation “ We are looking for sea changes in the web of data similar to what we saw in the web of documents. The simple clicking of a link from one document/location to another changed the way people operate.” … “We are looking for an even greater sea change in the web of data – we will be able to solve some problems we just couldn’t before – we will have machine power behind the data analysis. This is how we can create the correlations between massive amounts of data to reveal that clue to making that very serious step forward.” Tim Berners-Lee

3. 3 RSNA2007, November 25-30 Motivation There is a growing web of open medical knowledge bases available Appropriateness Guidelines Radlex Medical Lexicon Galen Medical Ontology Semantic Web technologies make that data machine readable and accessible Software agents backed by massive computing power to reason over these knowledge bases and create correlations that could not be realized before Promotes the sharing and central management of peer reviewed, evidence based, quality data

4. 4 RSNA2007, November 25-30 Motivation Radiological procedures often come with harmful side effects and high costs Investigations that are un-necessary or performed to frequently investigations that result in little or no impact on patient management investigations that are inappropriate for the indications Unnecessary irradiation In each case, scarce medical resources are wasted Dangerous use of Radiology Contra-indication Contrast agent that causes renal failures, trigger allergy reaction Irradiation on fetus MRI for patients with peace-maker and in-plants Excessive radiation exposure

5. 5 RSNA2007, November 25-30 Limitations of Commercial Systems Limitations of Commercial Medical Knowledge systems, such as CPOE’s (Computerized Order Entry) Based on proprietary knowledge bases customized to a specific application Incorporate some referral guidelines and appropriateness criteria Slow to reflect updates from the medical community Quality of knowledge is questionable Cannot harness evidence based imaging derived from real-time practice

6. 6 RSNA2007, November 25-30 The Big Picture

7. 7 RSNA2007, November 25-30 Key Benefits of Using Semantic Web Technology Support a natural separation of general medical knowledge captured in appropriateness guidelines, and the adaptation rules that denote local and execution context. This separation allows knowledge bases to be developed and validated by professional bodies, resulting in better credibility and ease of maintenance. Ease the burden of developing and maintaining a “complete” knowledge base by one medical organization or vendor. Provide a standards-based, application neutral platform for expressing and connecting to the existing corpus of knowledge. Supports evidence based imaging by combining medical knowledge with patient values from real time practice to provide recommendations at the point of care.

8. 8 RSNA2007, November 25-30 Evidence Based Imaging We demonstrate a generic decision support framework based on emerging Semantic Web technology, in the form of a Knowledge Mediated Order Entry application Standards based, application neutral Semantic Web Technology stack developed by the W3C Semantically accessible medical knowledge can be injected anywhere in the medical workflow – for our demonstration purposes, the knowledge is injected at order entry Makes use of open, shared knowledge bases managed by the appropriate medical bodies Confidence in quality of knowledge Fast incorporation of changing medical guidelines and protocols Incorporates real time practice data Patient values, accumulative radiation dosage Imaging frequency

9. 9 RSNA2007, November 25-30 Knowledge Based Radiology Order Entry System Introduce the use of emerging semantic web technology in Radiology Propose a standards-based, application neutral framework that can be used to integrate clinical knowledge and expertise with patient values for the best decision support URI, XML, RDF, OWL Ontology Converting ACR appropriateness to ACR2007 ontology Mapping to RadLex ontology and Galen ontology Ontology segmentation Browsing and query Rules Contra-indication checking Radiation dosage alert Generic, open source reasoning/inference engines CWM (Closed World Model) – forward chaining general purpose reasoner Euler – backward chaining reasoner

10. 10 RSNA2007, November 25-30 ACR Appropriateness Criteria

11. 11 RSNA2007, November 25-30 RPGOntology

12. 12 RSNA2007, November 25-30 Mapping to Radlex owl:equivalentClass

13. 13 RSNA2007, November 25-30 Segmentation of Galen Extract applicable information from large ontology Large ontology contains well-formed knowledge about a subject, maintained by the appropriate medical body Large size often prevents the ontology from being used in decision support systems Developed a segmentation technique to make the ontology more usable Segmentation of Galen ontology Derived automatic extract algorithm to extract segment (Head and Neck anatomy, Pathological Phenomenon) Preserve the logical relationship and soundness of the segmented ontology Use resulting ontology for reasoning in Knowledge Mediated Order Entry demonstration

14. 14 RSNA2007, November 25-30 Rules Contra-indication alert Contrast agent that causes renal failures, trigger allergy reaction All UMMS CT procedures, body parts, and contrast information available Simulated patient data for renal function, and allergy for demo Irradiation on fetus Radiation dosage available for CT procedures Simulated patient pregnancy data for demo purpose MRI for patients with peace-maker and in-plants (no data for the demo) Reimbursement Rules No same study within X weeks ( we have data back to May, 2007)

15. 15 RSNA2007, November 25-30 Integrating Patient/Population Data Harvest live patient data from IMPAX Back Office Research interface Accumulative CTDI per patient, per body part Histogram of CTDI dosage per demographic group (age + sex + body part) Alert during new order entry if patient in a high dose percentile

16. 16 RSNA2007, November 25-30 RPGDemo – Patient Selection

17. 17 RSNA2007, November 25-30 RPGDemo – Select Procedures

18. 18 RSNA2007, November 25-30 RPGDemo – Indication Selection Selecting anatomy based on RadLex ontology Display relevant pathology (galen ontology) based on the selection of body part

19. 19 RSNA2007, November 25-30 RPGDemo – Select Policy Selecting and de-selecting different validation rules to fit institutional/personal situation, get response of order validation appropriateness score Accumulative radiation dosage on the selected body part, also display the percentile the patient’s dosage among all his peers of the same demographic range

20. 20 RSNA2007, November 25-30 RPGDemo – Patient Safety and Dosage Monitoring Histogram of CTDI among peer group and patient’s CTDI on the specific body part CTDI Range Count

21. 21 RSNA2007, November 25-30 Challenges Ahead A sustainable model for medical standardization organizations to publish standards in Semantic web standards to enable unambiguous machine interpretation. Closer collaboration between knowledge community and information system vendors on open source, standards-based projects to generate high quality public knowledge bases that can be used/reused in many application domains. Exchange of rules, proofs in a standard exchange format W3C RIF group is currently working on the Rule Exchange Format task Semantic annotations to existing datasets for machine reasoning.