Harnessing Evidence Based Imaging An Architecture Based on Semantic Web Technology, Medical...

Harnessing Evidence Based ImagingAn Architecture Based on Semantic Web Technology, Medical Ontologies,

Reasoning Engines, and Pluggable Rule Sets

Helen H. Chen, PhD1, Glenn R. Potter1, Paul G. Nagy, PhD2 and Daniel L. Rubin, MD3

1Agfa Healthcare, 2University of Maryland Medical Systems, 3Stanford University

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


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


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


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


The Big Picture


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.


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


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


ACR Appropriateness Criteria


RPGOntology


Mapping to Radlex

owl:equivalentClass


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


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)


Integrating Patient/Population Data

PACS Application Server

PACS Back Office / Research Interface

Local Clinical DataProcessing Component

Local PopulationData in RDF

form

CTDI data by bodypart + age + sex

Population data compiled toRDF for use by Web of Proof

Engines

WEB of Proof Engines

PopulationDosage

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


RPGDemo – Patient Selection


RPGDemo – Select Procedures


RPGDemo – Indication Selection

Selecting anatomy based on RadLex

ontologyDisplay relevant pathology (galen ontology) based on the selection of body part


RPGDemo – Select PolicySelecting 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


RPGDemo – Patient Safety and Dosage Monitoring

Histogram of CTDI among peer group and patient’s CTDI on the

specific body part

CTDI RangeC

oun

t


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.

Harnessing Evidence Based Imaging An Architecture Based on Semantic Web Technology, Medical...

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