1. VIEWS-08-495b.ppt-1 Managing Intelligent Decision Support Networks in Biosurveillance PHIN 2008, Session G1, August 27, 2008 Mohammad Hashemian, MS, Zaruhi Mnatsakanyan, PhD The Johns Hopkins University Applied Physics Laboratory (JHU/APL)

2. VIEWS-08-495b.ppt-2 Agenda  Background  Goals  Intelligent Decision Support System  Results  Conclusion  Q&A

3. VIEWS-08-495b.ppt-3 Background  Automated Disease Surveillance  Main purpose: early detection  Challenge: false alarms  Electronic Medical Records  Challenge: manage large data volume and diverse data types  Fusion Based Models  Decision-support tools enhance identification of disease outbreaks utilizing multiple data types and sources  Software Framework  Limitation: model specific

4. VIEWS-08-495b.ppt-4 GOAL  Develop a generic framework for defining and processing decision-support models.  Integrate diverse computational models.  Implement GRID service and standalone version of the application.

5. VIEWS-08-495b.ppt-5 Requirement  Create a software framework that is:  Model Independent

6. VIEWS-08-495b.ppt-6 Requirement  Create a software framework that is:  Model Independent  Autonomous models

7. VIEWS-08-495b.ppt-7 Requirement  Create a software framework that is:  Model Independent  Autonomous models  Composite models (5) SeverityNet

8. VIEWS-08-495b.ppt-8 Reference Exploring Electronic Medical Records for Disease Surveillance (H3 8:30-10:00) Zaruhi R. Mnatsakanyan, PhD

9. VIEWS-08-495b.ppt-9 Requirement  Create a software framework that is:  Model Independent  Autonomous models  Composite models  Distributed models

10. VIEWS-08-495b.ppt-10 Reference Distributed Information Fusion Networks as a Tool to Facilitate Regional Collaboration without Data Sharing (I6 10:00-11:30) Zaruhi R. Mnatsakanyan, PhD

11. VIEWS-08-495b.ppt-11 Requirement  Create a software framework that is:  Model independent  Autonomous models  Composite models  Distributed models  Time Efficient  Access the data  Process the model  Deliver the result  Cost Effective  Define new models  Modify existing models

12. VIEWS-08-495b.ppt-12 Requirement  Masks Complexity  Manageable by end-users  Incorporate new and modify existing models  Set up new data source  Process data extraction algorithms  Minimize the need for IT expertise  Supports Collaboration  Experts from different domains

13. VIEWS-08-495b.ppt-13 Framework  Intelligent Decision Support System (IDSS)  Software framework for processing models as well as managing the interaction and information exchange among disparate models Data Source Configuration File Intelligent Decision Support Network (IDSN)

14. VIEWS-08-495b.ppt-14 Intelligent Decision Support Network Intelligent Decision Support Network (IDSN)  Intelligent Decision Support Network (IDSN) - A network of one or more fusion based models such as Bayesian Networks  ISDN provides a common interface for interacting and processing all fusion based models (Autonomous, Composite, Distributed)  Goals:  Dynamic and flexible  Parallel processing  Plug and play

15. VIEWS-08-495b.ppt-15 Sample IDSN  SeverityNet IDSN (5) SeverityNet

16. VIEWS-08-495b.ppt-16 IDSN Manager  Java-based application :  Collects the data (IDSS Users define the algorithms)  Query a database  Processes the models (IDSS Users describe the process)  Process Bayesian Networks (BN) in order of ChronicNet, HighRiskNet, ProviderBehaviorNet, RaiologyNet, and finally SeverityNet  Manages the communication between the models  Pass the results of RadiologyNet model to SeverityNet model  Delivers the results to other systems  Return a list of probabilities per day

17. VIEWS-08-495b.ppt-17 Data Source  Access multiple data sources  Each model within an IDSN can run on different data source  Each input node within a model can access a different data source  Store the Results  Internal communication – Store models’ results for other models use  External communication – Store IDSN’s result for other systems

18. VIEWS-08-495b.ppt-18 Configuration File  IDSN components  Ordered list of models  (1) ChronicNet, (2) HighRiskNet, (3) ProviderBehaviorNet, (4) RadiologyNet, (5) SeverityNet  Model  Model’s inputs  Age, ICD9_counts, Lab_counts  Data acquisition methods  Database query  Result  Model’s outputs  Radiology, Chronic, Severity  Storage  Database, Text file, Excel  Parameters and constraints  Geographical constraints  Time constraints  Data elements ( ICD9, Symptom,…)  Storage  Location (database name and server)  Credentials (user id and password)

19. VIEWS-08-495b.ppt-19 IDSS Process Flow Configuration File Next Model Network Properties Database QueryDetection Output Nodes Database Process the Model Calculate Input Node Values Retrieve Dataset Network Processor Process Method Run Detection on Model’s Nodes

20. VIEWS-08-495b.ppt-20 Grid Service JHU/APL Demo Client Web Application / Portal Requests Interaction Data Management User Interfaces Future PHGrid Resources Computational

21. VIEWS-08-495b.ppt-21 Scenarios (same dataset)Processing Time Processed sub-models one after the other before running the Severity model 2.5 Minutes All the sub-models processed in parallel before running the Severity model 1.2 Minutes Only processed the Severity model40 Seconds Results (Composite Model) (5) SeverityNet

22. VIEWS-08-495b.ppt-22 Results (Distributed Model) Scenarios (same dataset)Processing Time Processed all models in sequence1.3 minutes Parallel processed all models20 seconds

23. VIEWS-08-495b.ppt-23 Conclusion  Intelligent Decision Support System is a generic software framework  Processes any model regardless of  Structure  Domain  Data source  Provides optimal execution time for all model types  Parallel processing  Plug and play  Encourages a collaborative environment  supports rapid development and modification of decision- support models

24. VIEWS-08-495b.ppt-24 This presentation was supported by Grant Number P01 HK000028-02 from the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.