Transcript knowledge-based visualization of time

Distributed, Knowledge-Based
Temporal-Abstraction Mediation
Yuval Shahar, M.D., Ph.D.
Medical Informatics Research Center
Department 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
PAZ protocol
BMT
Expected CGVHD
M[0]
Platelet
counts
   
• • • •
(• )
150K
100K
0
50
100
M[1] M[2] M[3]
.
M[1]

     
• 
• •
• • •

•
• • •
200
Time (days)

•
M[0]
Granulocyte
counts
 
• • •
()
2000
1000
400
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)
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)
ee
ss
es
se
Induction of Interpretation Contexts
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)
t
Bel(j)
j1
j2
I1
I2
1
jth
0
Time
Abstraction of Periodic Patterns
Periodic Pattern
Linear Component
Fever
Anemia
Week 1
Temperature
Hemoglobin
Fever
Linear Component
Linear Component Linear Component
Fever
Fever
Anemia
Week 2
Anemia
Fever
Anemia
Week 3
The RÉSUMÉ System Architecture
.
Temporal-abstraction mechanisms
Domain TA knowledge base
Temporal fact base
E v e n ts
Event ontology
C o n te x ts
Context ontology
A b s tr a c te d in te r v a ls
Parameter ontology
P r im itiv e d a ta
External patient database
Events
Primitive data
•
+
•
+ +•
• +
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
Constant
Population
distribution
Abstractions
State
abstractions
Gradient
abstractions
Primitive
Rate
abstractions
Physical
Height
Radiology
Tanner
Boneage
HTSDS
HTSDS_state
HTSDS_STATE_STATE
(alarm states)
HTSDS_gradient
HTSDS_rate
Tanner_state
(Tanner SD)
Boneage_state
(boneage SD)
Maturation
Monitoring of Children’s growth:
Temporal Abstraction of the
Height Standard Deviation Score (HTSDS)
The Diabetes Parameter Ontology
Parameters
Laboratory
Abstract
Glucose
State abstractions
Glucose_state
Glucose_state_DM
Glucose_state_DM_preprandial
Glucose_state_DM_prebreakfast
Maximal-gap
functions
= PROPERTY-OF relation;
Temporalsemantic
properties
Horizontalclassification
tables
= IS-A relation;
Verticalclassification
tables
= ABSTRACTED_INTO relation
The Diabetes Event Ontology
= PART-OF relation;
= IS-A relation
The Diabetes Context Ontology
= SUB-CONTEXT relation;
= IS-A relation
Forming Contexts in Diabetes
Diabetes m ellitus (DM) treatm ent
Regular_ins ulin adm inis tration event
+10 hrs
+0.5 hrs
Regular_ins ulin_action
DM_regular_ins ulin_action interpretation context
Meal
+1 hrs
-1 hrs
0 hrs
Preprandial context
DM_preprandial context
0 hrs
Pos tprandial context
DM_Pos tprandial 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
DB
TM
Clinical
decision-support
application
TR
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 timeoriented goals, or visualization of temporal patterns
in the patient’s record
Two Possible Implementation Strategies
Application
Application
Temporal Data Management
Temporal Data Management
Database
1) Extend the DBMS
Database
2) Extend the Application
Problems in Extending The DBMS
Application
Temporal data management
methods implemented in a
DBMS:
 are limited to producing very
simple abstractions
 are often database-specific
Temporal Data Management
Database
Problems in Extending the Application
Temporal data
management methods
implemented in
applications:
Application
Temporal Data Management
 duplicate some of the
functions of the DBMS
 are application-specific
Database
Our Strategy
• Separates data
management methods
from the application
and the database
• Decomposes temporal
data management into
two general tasks:
Application
Temporal Abstraction
Temporal Querying
– temporal abstraction
– temporal maintenance
Database
The Tzolkin Temporal-Mediator Architecture
[Nguyen, Shahar et al., 1999]
Application
Query
Results
Tzolkin
Knowledge
Base
Temporal
Abstraction
Module
TemporalQuerying
Module
Abstraction
Knowledge
Database
The IDAN Temporal-Abstraction Mediator
(Boaz and Shahar, 2003, 2005)
Knowledge
Service
Knowledgeacquisition tool
Medical
Expert
Standard Medical
Vocabularies Service
Data Access
Service
TemporalAbstraction
Controller
KNAVE-II
Clinical
User
Temporal
Abstraction
Service
(ALMA)
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
Local data source site
Term mapping table
2: get local term and unit
(StdTerm )
3: LocalTerm, LocalUnit
1: Data request (
4: Data request(
Patient,
Patient, LocalTerm
Virtual
StdTerm, OutUnit )
5: Data
Data
module
schema
?
adaptor
access
9: Result
)
Transformation
6: get transformation
functions library
function( LocalUnit, OutUnit
)
(DAM)
Unknown
7: TransFunc
schema
8: Result = transform
(Data, TransFunc
)
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]