Drools – Diabetes Phenotype
Identification
June 30, 2011
Jeffrey Ferraro, PhDc1
Herman Post, MS1
Darin Wilcox, MS1
1Intermountain
Healthcare, Salt Lake City, Utah
Discussion Points
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Drools Background
Drools Inference & Workflow Capabilities
Describe Drools Inference Execution Model
Exam the Drools Rule Semantics
High Level Architecture
Diabetes Workflow
Perceived Benefits
Future Directions
Drools Framework
 Business Logic Integration Platform
 Framework Supports
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Production Rule Inference Engine
Workflow (jBPM ≡ Workflow Engine)
Complex Event Processing (CEP)
Planner – Optimization of NP-hard problems in
workflow (i.e. time scheduling)
Drools History
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SourceForge Open Source in 2001
Licensed under Apache
Part of JBoss Project in 2005
RedHat acquires JBoss in 2006
JBoss Rules (Commercially Supported Version)
Published Books
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JBoss Drools Business Rules, Paul Browne
Drools JBoss Rules 5.0 Developer's Guide, Michal Bali
Business Process Management with JBoss jBPM: A Practical
Guide for Business Analyst, Matt Cumberlidge
Drools Productivity Tools
 Guvnor – Business Rules Management System (BRMS)
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Authoring
Testing
Versioning
Access Control of Rules & Workflow
 Eclipse plugins
 Authoring rules and workflow
 Integration into Guvnor
 Decision Tables
 Rule or Workflow Testing Structures
Authoring Languages
 Drools Rule Language (DRL) – Native Rule Syntax
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Two Language Dialects
 MVEL – Expression Language
 Java – Programming Like
 Business Process Management Notation v2 (BPMN2)
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Workflow Authoring
 Domain Specific Language Creation (DSL)
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Human Readable Grammars
*Production Rules Definition Only
Benefits of a Rules Engine
 Declarative Programming Model (‘what’ not ‘how’)
 Promotes Separation of Business Logic and Data
 Loose Coupling of Business Logic (Reusability)
 Promote Centralization of Knowledge
 Productivity: Knowledge Engineering Tools
 Strive for Understandable Rules (Human Readable?)
 Shareable Rules Facilitated by Common Fact Model
Drools Inference Engine
 Represent Knowledge thru Production Rules
 Forward Chaining Inference Model
 Rule Matching(‘how’): Rete Algorithm & Leaps
 “Data Driven” – Reactionary Inference Model
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Facts Asserted into Working Memory
Rule Propagation takes Place
Legible Rules Schedule for Execution
Consequence(s) Realized (Results)
 Backward Chaining Inference Model (future support)
 “Goal Driven” (Why Questions)
 Start with Conclusion  Inference to Satisfy Conclusion
Drools Workflow
 Goal of Workflow - combine tasks into an understandable
process
 Supports task sequencing
 Promotes understanding thru process visualization - graphical
modeling support
 Provides seamless integration of rules and flow
 Supports WS HumanTask 1.0 implementation
 State Management
 Supports Pluggable Work Items
 Domain-specific Definition
 Declarative Model (‘what’, not ‘how’)
 High-level Definition (no code)
Drools Inference Architecture
Inference Execution Model
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Define a Knowledge Base
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Compiled Rules (*Expensive Operation)
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Produces Production Memory
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Extract Knowledge Session from Knowledge
Base
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Insert Facts (data) into Knowledge Session 
“Agenda”
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Fire Rules (*Race Conditions/Infinite Loop –
ouch!)
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Retrieve End Results
DRL Basic Rule Semantics
rule <name>
when {Condition}
<(1..n) facts in Working Memory OR data from external
sources, matches this rule>
then {Consequence}
<update, insert or retract some facts in Working Memory
OR set a parameter (end result)>
end
Example DRL Rule
rule
"Glucose <= 40, Insulin On“
when
$msg : GlucoseMsg(glucoseFinding <= 40, currentInsulinDrip > 0 )
then
glucoseProtocolResult.setInstruction(GlucoseInstructions.GLUCOSE
_LESS_THAN_40_INSULIN_ON_MSG);
end
Example DRL Rule
{Rule Name}
rule
"Glucose <= 40, Insulin On“
{Class Getter Method}
{binding}
{Java Class}
when
$msg : GlucoseMsg(glucoseFinding <= 40, currentInsulinDrip > 0 )
then
glucoseProtocolResult.setInstruction(GlucoseInstructions.GLUCOSE
_LESS_THAN_40_INSULIN_ON_MSG);
end
Parameter {Java Class}
{Class Setter Method}
Current Architecture
Clinical
Element
Database
Data Access
Layer
Business Logic
Transformation
Layer
Transform physical representation
 Normalized logical representation
(Fact Model)
Inference
Engine (Drools)
Service for
Creating Output
(File, Database,
etc)
List of
Diabetic
Patients
Diabetes Workflow
Benefits
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Organized representation of knowledge & process
Rules and Workflow - accessibility by clinicians
Facilitates collaborative authoring
Potential for knowledge sharing thru common fact
model
 BPMN represents an explicit, declarative stateful
programming model
 Tight integration of rules and workflow
Future Directions
 Define a fact model that can be shared across
organizations
 Better understand use of Rules vs. Workflow
 Explore Domain Specific Languages (DSL)
 Evaluate performance & scalability
 Define a Rules and Workflow sharing model
Collaboration & Exploration
 Drools Knitting Group (hosted by Intermountain Healthcare)
 Participants
 University of Utah
 Duke University
 Naval Medical Center (Emory Frye)
 Shared Experimental Environment
Thank You!