A Proté ég gé é Ontology as The Core Ontology as The Core A Prot Component of a BioSense Component of a BioSense Message Analysis Framework Message Analysis Framework Cecil Lynch 1,2 , Craig Cunningham 1 , Eric Cecil Lynch 1,2 , Craig Cunningham 1 , Eric Schripsema 1 1 , Tim Morris , Tim Morris 3 3 , Barry Rhodes , Barry Rhodes 3 3 Schripsema 1 1 1 OntoReason,LLC, 2 UC Davis, 3 US Centers for Disease Control and Prevention
Outline Outline • BioSense description BioSense description • • Describe the current environment Describe the current environment • • Describe the ontology Describe the ontology • • Describe the ontology framework Describe the ontology framework • • Describe the analysis workbench Describe the analysis workbench • • Future directions Future directions • • Questions Questions • 2 2
BioSense Description BioSense Description 3 3
What is BioSense? What is BioSense? • Real Real- -time and near real time and near real- -time national public health time national public health • message analysis framework message analysis framework • Consists of Consists of • • Message acquisition and translation interfaces • Message acquisition and translation interfaces • Secure message transmission network Secure message transmission network • • Message classification components Message classification components • • Data storage and query components Data storage and query components • • Data analysis component • Data analysis component • CDC Monitors CDC Monitors • • Local data visualization and distribution Local data visualization and distribution • 4 4
BioSense Functions BioSense Functions Confirm or refute existence of an event Confirm or refute existence of an event Environmental signal � � Environmental signal Suspect illness � Suspect illness � � Intelligence warning Intelligence warning � � Known outbreak/public health event � Known outbreak/public health event Monitor ongoing event and effectiveness of response Monitor ongoing event and effectiveness of response � Ascertain size of event Ascertain size of event � � Ascertain rate of spread � Ascertain rate of spread Track efficacy of response efforts � � Track efficacy of response efforts Monitor for adverse events � Monitor for adverse events � � Know when an event has passed Know when an event has passed � 5 5 CDC Slide
Data Sources Data Sources Data Source Rationale Data Source Rationale 2006 2006 Orders & results from 3 Represent 20% of all US lab testing; 60% of Orders & results from 3 Represent 20% of all US lab testing; 60% of independent testing; critical to many PH independent testing; critical to many PH major commercial major commercial efforts efforts clinical laboratories clinical laboratories Real- -time data from VA time data from VA 150 hospitals and ~1000 ambulatory care Real 150 hospitals and ~1000 ambulatory care clinics; share data with many state and local clinics; share data with many state and local PH communities PH communities Real- -time data from time data from 45 US hospitals and ~800 ambulatory; Real 45 US hospitals and ~800 ambulatory; share data DoD DoD share data Poison Control Centers All 62 poison control centers; display and Poison Control Centers All 62 poison control centers; display and compare with other community health call data call data compare with other community health data data Private Hospitals 500 Clinical care Hospitals provide Private Hospitals 500 Clinical care Hospitals provide national view and local data national view and local data 6 6 CDC Slide
Target Data Types Target Data Types • Foundational* Foundational* : : demographics, chief complaint, discharge demographics, chief complaint, discharge • diagnoses, disposition, hospital utilization diagnoses, disposition, hospital utilization • Clinical* Clinical* : : vitals, triage notes, working diagnosis, discharge vitals, triage notes, working diagnosis, discharge • summary summary • Laboratory Laboratory : : orders, microbiology results orders, microbiology results • • Pharmacy Pharmacy : : medication orders medication orders • • Radiology Radiology : : orders, interpretation results orders, interpretation results • All structured in HL7 2.5 BioSense messages All structured in HL7 2.5 BioSense messages 7 7 CDC Slide
Current Classification Current Classification • Data mapped to 11 syndrome categories Data mapped to 11 syndrome categories • • Botulism Botulism- -like like • • Fever Fever • • Gastrointestinal Gastrointestinal • • Hemorrhagic illness Hemorrhagic illness • • Localized cutaneous lesion Localized cutaneous lesion • • Lymphadenitis Lymphadenitis • • Neurological Neurological • • Rash Rash • • Respiratory Respiratory • • Severe illness/death Severe illness/death • • Specific infection Specific infection • • 79 sub 79 sub- -syndrome categories syndrome categories • 8 8 CDC Slide
Watch what you ask for! Watch what you ask for! • BioSense message volume capacity today BioSense message volume capacity today • • 837 messages a second 837 messages a second • • >72 million messages a day >72 million messages a day • • How does an epidemiologist review that How does an epidemiologist review that • volume of data? volume of data? • How do you link messages to an individual How do you link messages to an individual • over time to refine the diagnostic info? over time to refine the diagnostic info? 9 9
Current BioSense Framework Current BioSense Framework 10 10
11 11 Message Processing
12 12 Load Balancing
13 13 Message Type Filter
14 14 ETL Processing
15 15 AV and OTP
16 16 End User Views
The OntoReason PH Ontology The OntoReason PH Ontology 17 17
18 18 Engine Rule Engine Rule Engine Rule Engine Rule Model Profile Applications Ontology
19 19
20 20
Information Model Information Model 21 21
Concept In HL7 V3 DataType DataType Concept In HL7 V3 Code Term Parent Other code Systems and synonyms Children BioSense Terms 22 22
Conceptual and Syntactical Conceptual and Syntactical Level Level 23 23
HL V3 Class Object HL V3 Class Object Frequency for each object References for each object 24 24
Clinical Domain Object Clinical Domain Object Nested MetaClass 25 25
26 26
Laboratory Observation HL7 Laboratory Observation HL7 V3 mapped to V2 V3 mapped to V2 OBX-3 OBX-17 SPM-4 OBX-8 OBX-7 OBX-5 27 27
Map HL7 Message segments to Map HL7 Message segments to Ontology Slots Ontology Slots 28 28
Ontology Services Platform Ontology Services Platform 29 29
Technical Foundations Technical Foundations • Enterprise PHIN SOA Platform • Web Services Models • Application Libraries • LexPHIN Database • Individual Reasoners Patterns - Languages Application • Intelligence & Analytics Workbench - Tools Models • CTS & LexPHIN Services - Standards • PH Reference Ontology Domain Message • PHIN VS Models Structure • BioSense Msg HL7 V2.x 30 30
Ontology Extraction Ontology Extraction • Creating an application ontology from the reference ontology • Creating an application ontology from the reference ontology • Identify the core ontology classes Identify the core ontology classes • • Create an object representation that maintains the ontology data Create an object representation that maintains the ontology data • • Generate cross reference indexes for core relationships Generate cross reference indexes for core relationships • • Lab tests to case investigations Lab tests to case investigations • • Organism/Agent to case investigations Organism/Agent to case investigations • • Other significant relationships Other significant relationships • • Identify Identify “ “Used Used” ” vocabulary vocabulary • • Create vocabulary subsets that identify specific vocabularies Create vocabulary subsets that identify specific vocabularies • concepts that are used within the ontology concepts that are used within the ontology • • Create code to code mapping indexes Create code to code mapping indexes • • This produces a general purpose extraction that is suitable for This produces a general purpose extraction that is suitable for various various purposes purposes
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