1. Drools – Diabetes Phenotype Identification
June 30, 2011
Jeffrey Ferraro, PhDc1
Herman Post, MS1
Darin Wilcox, MS1
1Intermountain Healthcare, Salt Lake City, Utah

2. Discussion Points 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

3. Drools Framework Business Logic Integration Platform
Framework Supports
Production Rule Inference Engine
Workflow (jBPM ≡ Workflow Engine)
Complex Event Processing (CEP)
Planner – Optimization of NP-hard problems in workflow (i.e. time scheduling)

4. Drools History 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
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

5. Drools Productivity Tools
Guvnor – Business Rules Management System (BRMS)
Authoring
Testing
Versioning
Access Control of Rules & Workflow
Eclipse plugins
Authoring rules and workflow
Integration into Guvnor
Decision Tables
Rule or Workflow Testing Structures

6. Authoring Languages Drools Rule Language (DRL) – Native Rule Syntax
Two Language Dialects
MVEL – Expression Language
Java – Programming Like
Business Process Management Notation v2 (BPMN2)
Workflow Authoring
Domain Specific Language Creation (DSL)
Human Readable Grammars
*Production Rules Definition Only

7. 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

8. Drools Inference Engine
Represent Knowledge thru Production Rules
Forward Chaining Inference Model
Rule Matching(‘how’): Rete Algorithm & Leaps
“Data Driven” – Reactionary Inference Model
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

9. 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)

10. Drools Inference Architecture
Inference Execution Model
Define a Knowledge Base
Compiled Rules (*Expensive Operation)
Produces Production Memory
Extract Knowledge Session from Knowledge Base
Insert Facts (data) into Knowledge Session  “Agenda”
Fire Rules (*Race Conditions/Infinite Loop – ouch!)
Retrieve End Results

11. 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

12. 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

13. Example DRL Rule
{Rule Name}
rule "Glucose <= 40, Insulin On“
when
$msg : GlucoseMsg(glucoseFinding <= 40, currentInsulinDrip > 0 )
then
glucoseProtocolResult.setInstruction(GlucoseInstructions.GLUCOSE _LESS_THAN_40_INSULIN_ON_MSG);
end
{Class Getter Method}
{binding}
{Java Class}
Parameter {Java Class}
{Class Setter Method}

14. Current Architecture Business Logic Clinical Element Database
Data Access Layer
Business Logic
Transformation Layer
Inference Engine (Drools)
List of
Diabetic Patients
Service for Creating Output (File, Database, etc)
Transform physical representation
 Normalized logical representation (Fact Model)

15. Diabetes Workflow

16. Benefits 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

17. 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

18. Collaboration & Exploration
Drools Knitting Group (hosted by Intermountain Healthcare)
Participants
University of Utah
Duke University
Naval Medical Center (Emory Frye)
Shared Experimental Environment

19. Thank You!