Drools – Diabetes Phenotype Identification

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?) • ShareableRules 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 MemoryOR data from external sources, matches this rule> then {Consequence} <update, insert or retract some facts in Working Memory ORset 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 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 thrucommon fact model • BPMN represents an explicit, declarative statefulprogramming model • Tight integration of rules and workflow

17. Future Directions • Define afact 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!