Distributed, Knowledge-Based Temporal-Abstraction
Mediation
Yuval Shahar, M.D., Ph.D.
Medical Informatics Research CenterDepartment of Information Systems Engineering
Ben Gurion University, Beer Sheva,
Israel
The Need for Intelligent Integration of Multiple Time-Oriented Clinical Data
• Many medical tasks, especially those involving chronic patients, require extraction of clinically meaningful concepts from multiple sources of raw, longitudinal, time-oriented data– Example: “Modify the standard dose of the drug, if during treatment,
the patient experiences a second episode of liver toxicity (Grade II or more) that has persisted for at least two weeks”
• Examples of clinical tasks:– Diagnosis
• Searching for “a gradual increase of fasting blood-glucose level”– Therapy
• Following a treatment plan based on a clinical guideline– Quality assessment
• Comparing observed treatments with those recommended by a guideline– Research
• Detection of hidden dependencies over time between clinical parameters
The Need for Intelligent Mediation:The Gap Between Raw Clinical Data and Clinically Meaningful Concepts
• Clinical databases store raw, time-stamped data• Care providers and decision-support applications
reason about patients in terms of abstract, clinically meaningful concepts, typically over significant time periods
• A system that automatically answers queries or detects patterns regarding either raw clinical data or concepts derivable from them over time, is crucial for effectively supporting multiple clinical tasks
The Temporal-Abstraction Task
• Input: time-stamped clinical data and relevant events (interventions)
• Output: interval-based abstractions
• Identifies past and present trends and states
• Supports decisions based on temporal patterns, such as: “modify therapy if the patient has a second episode of Grade II bone-marrow toxicity lasting more than 3 weeks”
• Focuses on interpretation, rather than on forecasting
A Clinical Temporal-Abstraction Example:The Bone-Marrow Transplantation
Domain
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•
0 40020010050
•
1000
2000( )
100K
150K
( )
•••
• • • ••
• •
•••
Granu-locytecounts
• • •
•
Time (days)
Plateletcounts
PAZ protocol
M[0] M[1] M[2] M[3] M[1] M[0]
BMT
Expected CGVHD
The Bone-Marrow Transplantation Example, Revisited
Uses of Temporal Abstractions:
Examples in BioMedical Domains• Therapy planning and patient monitoring; E.g., the EON and DeGel projects (modular architectures to support guideline-based care)
• Creating high-level summaries of time-oriented medical records
• Supporting explanation modules for a medical DSS
• Representing goals of therapy guidelines for quality assurance at runtime and quality assessment retrospectively; E.g., the Asgaard project: Guideline intentions regarding both process and outcomes are captured as temporal patterns to be achieved or avoided
• Recent use in Italy for detecting patterns in gene expression levels
• Visualization of time-oriented clinical data: the KNAVE project
Knowledge-Based Temporal Abstraction (KBTA)
Structuralknowledge
Classificationknowledge
Temporal-semanticknowledge
Temporal-dynamicknowledge
Contextformation
Temporalinference
Temporalinterpolation
Temporal-patternmatching
Contempo-raneousabstraction
The knowledge-basedtemporal-abstractionmethod
The temporal-abstraction task
Temporal-contextrestriction
Verticaltemporalinference
Horizontaltemporalinference
Temporalinterpo-lation
Temporal-patternmatching
The KBTA Ontology• Events (interventions) (e.g., insulin therapy) - part-of, is-a relations
• Parameters (measured raw data and derived concepts) (e.g., hemoglobin values; anemia levels) - abstracted-into, is-a relations
• Patterns (e.g., crescendo angina; quiescent-onset GVHD) - component-of, is-a relations
• Abstraction goals (user views)(e.g., therapy of diabetes) - is-a relations
• Interpretation contexts (effect of regular insulin) - subcontext, is-a relations
• Interpretation contexts are induced by all other entities
Temporal-Abstraction Output Types
• State abstractions (LOW, HIGH)• Gradient abstractions (INC, DEC)• Rate Abstractions (SLOW, FAST) • Pattern Abstractions (CRESCENDO)
- Linear patterns- Periodic patterns
Temporal-Abstraction Knowledge Types
• Structural (e.g., part-of, is-a relations) - mainly declarative/relational
• Classification (e.g., value ranges; patterns) - mainly functional
• Temporal-semantic (e.g., “concatenable” property) - mainly logical • Temporal-dynamic (e.g., interpolation functions) - mainly probabilistic
Dynamic Induction of Contexts:Temporal Constraints Between Inducing Proposition and Induced
Context(Shahar, AMAI 1998)
+2W+4W
AZT-toxicity interpretation context
AZT administration
ss
ee
sees
Induction of Interpretation Contexts
+2W+4W
CCTG-522_AZT-toxicity interpretation context
AZT-administration event
(a)
+4W+-6W
-2W
Hepatitis B
HB prodrome Chronic active hepatitis
(b)
CCTG-522 protocol
The Meaning of Interpretation Contexts
• Context intervals serve as a frame of reference for interpretation: Abstractions are meaningful only in a context (e.g., “anemia in a pregnant woman”)
• Context intervals focus and limit the computations to only those relevant to a particular context (thus, knowledge is brought to bear only when relevant)
• Contexts enable the use of context-specific knowledge, thus increasing accuracy of resultant abstractions
Advantages of Explicit Contexts•Any temporal relation (e.g., overlaps) can hold between a
context and its inducing proposition; contexts can be induced before and after the inducing proposition (thus enabling a certain type of hindsight and foresight)+ Note: Forming contexts is a finite process
• The same context-forming proposition can induce multiple context intervals
• The same interpretation context might be induced by different propositions
• Explicit contexts support maintenance of several concurrent views (or interpretations) of the data, in which the same parameter has different values at the same time, each within a different context+ Note: No contradiction--values are in different contexts
Local and Global Persistence Functions:Exponential-Decay Local Belief Functions
(Shahar, JETAI 1999)
1
0
I1 I2
t
1 2
th
Time
Bel()
Temperature
Hemoglobin Level
Linear Component
Week 2 Week 3Week 1
Anemia
Fever Fever
Anemia Anemia
FeverFever
Anemia
Fever
Linear ComponentLinear Component Linear Component
Periodic Pattern
Abstraction of Periodic Patterns
The RÉSUMÉ System Architecture
Temporal-abstraction mechanisms
Temporal fact baseE v e n t s
C o n t e x t s
A b s t r a c t e d i n t e r v a l s
P r i m i t i v e d a t a •
Domain TA knowledge base
Event ontology
Parameter ontology
Primitive data
Events
••
Context ontology
External patient database
+ +
+
•+
Application Domains for the KBTA Method(Shahar & Musen, 1993, 1996; Shahar & Molina 1999;
Boaz and Shahar 2005; Shabtai, Shahar, and Elovic, 2006)
• Medical domains:– Guideline-based care
• AIDS therapy
• Oncology
– Monitoring of children’s growth
– Therapy of insulin-dependent diabetes patients
• Non-medical domains:– Evaluation of traffic-controllers actions
– summarization of meteorological data
– Integration of intelligence data over time
– Monitoring electronic security threats in computers and communication networks
Monitoring of Children’s growth:The Parameter Ontology
Parameters
Abstract Primit ive
Abstractions
State
abstractions
Gradient
abstractions
Rate
abstractions
Physical Radiology
Tanner
HTSDS
Height Boneage
Maturation
HTSDS_state
HTSDS_STATE_STATE
(alarm states)
HTSDS_gradient HTSDS_rate
Constant
Population distribution
Tanner_state (Tanner SD)
Boneage_state (boneage SD)
Monitoring of Children’s growth: Temporal Abstraction of the
Height Standard Deviation Score (HTSDS)
HTSDS
0
2.0
4.0
6.0
-2.0
-4.0
ContextsFEMALE
Abstracted intervals
4 5 6 7 8 9 10
Age (years)
•
••
•
•
•
•
HTSDS_SS
HTSDS_G
HTSDS_R
MILD_ALARM SEVERE_ALARM
SAME INCREASING DECREASING
STATIC FAST STATIC FAST
The Diabetes Parameter Ontology
= PROPERTY-OF relation; = IS-A relation; = ABSTRACTED_INTO relation
Parameters
Abstract Laboratory
State abstractions Glucose
Glucose_state
Glucose_state_DM
Glucose_state_DM_preprandial
Glucose_state_DM_prebreakfast
Maximal-gapfunctions
Temporal-semanticproperties
Horizontal-classificationtables
Vertical-classificationtables
The Diabetes Event Ontology
= PART-OF relation; = IS-A relation
Events
Medications
Insulin
Regular_insulin NPH_insulin UL_insulin
Physical exerciseMeals
Warm-up Main-effort
Breakfast Lunch Supper Snack
The Diabetes Context Ontology
= SUB-CONTEXT relation; = IS-A relation
Contexts
Insulin_action
Regular_insulinaction
DM
Preprandial PostprandialPost_PE
Prebreakfast
Post_PE
Prelunch Presupper
Forming Contexts in Diabetes
+0.5 hrs+10 hrs
Regular_insulin_action
Regular_insulin administration event
Diabetes mellitus (DM) treatment
DM_regular_insulin_action interpretation context
Postprandial contextPreprandial context
+1 hrs
Meal
0 hrs
-1 hrs0 hrs
DM_Postprandial contextDM_preprandial context
Acquisition of Temporal-Abstraction Knowledge
(Shahar et al., JAMIA, 1999)
Evaluation of Automated Knowledge Entry
• Formal evaluation performed, using– 3 experts, 3 knowledge engineers, 3 clinical domains
– a gold standard of data, knowledge and output abstractions
• Domains: – monitoring of children’s growth
– care of diabetes patients
– protocol-based care in oncology and AIDS
• The study evaluated the usability of the KA tool solely for entry of previously elicited knowledge
KA Tool Evaluation: Results
• Understanding RÉSUMÉ required 6 to 20 hours (median: 15 to 20 hours)
• Learning to use the KA tool required 2 to 6 hours (median: 3 to 4 hours)
• Acquisition times for physicians varied by domain: 2 to 20 hours for growth monitoring (median: 3 hours), 6 and 12 hours for diabetes care, and 5 to 60 hours for protocol-based care (median: 10 hours)
• A speedup of up to 25 times (median: 3 times) was demonstrated for all participants when the KA process was repeated
• On their first attempt at using the tool to enter the knowledge, the knowledge engineers recorded entry times similar to those of the second attempt of the expert physicians entering the same knowledge
• In all cases, RÉSUMÉ, using knowledge entered via the KA tool, generated abstractions that were almost identical to those generated using the same knowledge, when entered manually
Editing The KBTA Ontology in Protégé 2000
Temporal Reasoning and Temporal Maintenance
• Temporal reasoning supports inference tasks involving time-oriented data; often connected with artificial-intelligence methods
• Temporal data maintenance deals with storage and retrieval of data that has multiple temporal dimensions; often connected with database systems
• Both require temporal data modelling
Clinicaldecision-supportapplication
TM TR DB
Examples of Temporal-Maintenance
Systems
• TSQL2, a bitemporal-database query language (Snodgrass et al., Arizona)
• TNET and the TQuery language (Kahn, Stanford/UCSF)
• The Chronus/Chronus2 projects (Stanford)
Examples of Temporal-Reasoning
Systems
• RÉSUMÉ
• M-HTP
• TOPAZ
• TrenDx
Temporal Data Manager
• Performs– - Temporal abstraction of time-oriented data
– - Temporal maintenance
• Used for tasks such as finding in a patient database which patients fulfils the guideline eligibility conditions (expressed as temporal patterns), assessing the quality of care by comparison to predefined time-oriented goals, or visualization of temporal patterns in the patient’s record
1) Extend the DBMS 2) Extend the Application
Two Possible Implementation Strategies
Database
Application
Temporal Data Management
Database
Application
Temporal Data Management
Problems in Extending The DBMS
Temporal data management methods implemented in a DBMS: are limited to producing very
simple abstractions are often database-specific
Database
Application
Temporal Data Management
Problems in Extending the Application
Temporal data management methods implemented in applications: duplicate some of the
functions of the DBMS are application-specific
Database
Application
Temporal Data Management
Our Strategy
• Separates data management methods from the application and the database
• Decomposes temporal data management into two general tasks:– temporal abstraction– temporal maintenance
Database
Application
Temporal AbstractionTemporal Querying
The Tzolkin Temporal-Mediator Architecture
[Nguyen, Shahar et al., 1999]
Database
Application
Temporal-QueryingModule
TemporalAbstraction
Module
KnowledgeBase
Tzolkin
ResultsQuery
AbstractionKnowledge
The IDAN Temporal-Abstraction Mediator(Boaz and Shahar, 2003, 2005)
Temporal-Abstraction
Controller
Knowledge- acquisition tool
Standard Medical Vocabularies Service KNAVE-II
Knowledge Service
Temporal-Abstraction
Service (ALMA)
Data Access Service
Medical Expert
Clinical User
Adding a New Clinical Database to The IDAN Mediator Architecture
• Due to local variations in terminology and data structure, linking to a new clinical database requires creation of– A schema-mapping table– A term-mapping table– A unit-mapping table
• The mapping tools use a vocabulary-server search engine that organizes and searches within several standard controlled medical vocabularies (ICD-9-CM , LOINC, CPT, SNOMED, NDF)
• Clinical databases are mapped into the standard terms and structure that are used by the clinical knowledge base, thus making the knowledge base(s) highly generic and reusable
• The overall mapping methodology has been implemented within the Medical Database Adaptor (MEIDA) system [German, 2006]
The LOINC Server Search Engine
LOINC Search Results
Accessing Local Data Sources
Unknown
schema
Virtual
schema
adaptorData
access
module
(DAM)
Term mapping table
Local data source site
4: Data request( Patient, LocalTerm )
3: LocalTerm, LocalUnit
5: Data
2: get local term and unit
(StdTerm )
?
6: get transformation
function( LocalUnit, OutUnit )
1: Data request ( Patient,
StdTerm, OutUnit )
7: TransFunc
8: Result = transform
(Data, TransFunc )
9: Result
Transformation
functions library
Summary:Knowledge-Based Abstraction
of Time-Oriented Data• Temporal abstraction of time-oriented data can employ reusable domain-
independent computational mechanisms that access a domain-specific temporal-abstraction ontology
• Temporal abstraction is useful for monitoring, therapy planning, data summarization and visualization, explanation, and quality assessment
• The IDAN distributed temporal mediator mediates and coordinates queries to the knowledge base and to the database
• Current and future work: – Continuous temporal abstraction - The Momentum architecture [Spokoiny and Shahar, 2004,
in press]– Probabilistic temporal abstraction (PTA) [Ramati and Shahar, 2005]