Drools – Diabetes Phenotype Identification

June 30, 2011 Jeffrey Ferraro, PhDc 1 Herman Post, MS 1 Darin Wilcox, MS 1 1 Intermountain Healthcare, Salt Lake City, Utah

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

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)

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

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

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

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   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  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

DRL Basic Rule Semantics

rule

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 when

"Glucose <= 40, Insulin On“ $msg : GlucoseMsg(glucoseFinding <= 40, currentInsulinDrip > 0 )

then

glucoseProtocolResult.setInstruction(GlucoseInstructions.GLUCOSE

_LESS_THAN_40_INSULIN_ON_MSG);

end

Example DRL Rule

rule when

{Rule Name}

"Glucose <= 40, Insulin On“

{binding} {Java Class} {Class Getter Method}

$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 Transformation Layer Transform physical representation  Normalized logical representation (Fact Model) Business Logic Inference Engine (Drools) Service for Creating Output (File, Database, etc) List of Diabetic Patients

Diabetes Workflow

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

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!