Distributed KnowledgeBased Abstraction, Visualization, and Exploration of TimeOriented Clinical Data

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Distributed Knowledge-Based Abstraction,
Visualization, and Exploration of Time-Oriented
Clinical Data
Yuval Shahar, M.D., Ph.D. Ben Gurion University,
Beer Sheva Stanford University, CA, USA
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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
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Temporal AbstractionThe Bone-Marrow
Transplantation Domain
PAZ protocol
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Time (days)
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Uses of Temporal Abstractions
Therapy planning and patient monitoring E.g.,
the EON and DeGel project (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 Visualization of
time-oriented clinical data the KNAVE project
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The Temporal-Abstraction 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
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
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Temporal-Abstraction Output Types
State abstractions (LOW, HIGH) Gradient
abstractions (INC, DEC) Rate Abstractions
(SLOW, FAST) Pattern Abstractions
(CRESCENDO) - Linear patterns - Periodic
patterns
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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
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Dynamic Induction Relations of Contexts
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Induction of Interpretation Contexts
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The Meaning of Interpretation Contexts

• Context intervals serve as a frame of reference
  for interpretation abstractions are meaningful
  only in a context
• Context intervals focus and limit the
  computations to only those relevant to a
  particular context
• Contexts enable the use of context-specific
  knowledge

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Advantages of Explicit Contexts
Any temporal relation 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) 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
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Local and Global Persistence Functions
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Abstraction of Periodic Patterns
Periodic Pattern
Linear Component
Linear Component
Linear Component
Linear Component
Week 2
Week 3
Week 1
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The RÉSUMÉ System Architecture
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Temporal-abstraction mechanisms
Domain knowledge base
Temporal fact base
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Event ontology
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Context ontology
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Parameter ontology


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External patient database
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Primitive data



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Test Domains for the RÉSUMÉ System

• Medical domains
• Guideline-based care
• AIDS therapy
• Oncology
• Monitoring of childrens growth
• Therapy of insulin-dependent diabetes patients
• Non-medical domains
• Evaluation of traffic-controllers actions
• summarization of meteorological data

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Acquisition of Temporal-Abstraction Knowledge
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Knowledge-Based Visualization andExploration of
Time-Oriented Medical DataThe KNAVE-I/KNAVE-II
Projects

• KNAVE Knowledge-Based Navigation of
  Abstractions for Visualization and Explanation
• Interactive composition of queries regarding both
  raw clinical data and multiple levels of
  time-oriented abstractions derivable from these
  data
• Visualization and interactive manipulation of
  query results
• Dynamic exploration of the results using the
  domains temporal-abstraction ontology (terms and
  relations)
• The semantics of all operators do not depend on
  any specific medical domain, but the interface
  uses each domains ontology to compute and
  display specific terms and explore their relations

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The KNAVE Architecture
Temporal Mediator
Expert
physician
Résumé
KA
DB mediator
Tool
Controller
Domain-knowledge Server
Vocabulary schema mappings
Ontology server
DBs
Visualization and
exploration module
Computational
KBs
Module
End user
Graphical
Interface
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The Browsing and Exploration Interface
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Zooming Into a Time Interval
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Semantic Exploration Operators
Motion across semantic links in the domains
knowledge base in particular, relations (and
their inverse) such as - part-of -
is-a - abstracted-from - subcontext
Motion across abstraction types state, gradient,
rate, pattern Application of aggregation
operators such as mean and distribution
Dynamic change of temporal-granularity (e.g.,
from days to months) changes the display,
using domain-specific aggregation knowledge
Explanation by display of relevant knowledge, or
through What- if queries, which allow
hypothetical assertion or retraction of data
or knowledge and examination of resultant patterns
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Semantic Exploration of Temporal Abstractions
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Explanation of Temporal Abstractions
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The KNAVE-I Preliminary Evaluation

• Developmental assessment of the stand-alone
  prototype
• Seven users with varying medical/computer use
  backgrounds
• Each user given a 10 minute introduction to the
  KNAVE-I system
• A single electronic patient file constructed from
  several cases in the domains of AIDS and
  bone-marrow transplantation
• Each user asked to perform three tasks (a complex
  temporal query, a context-sensitive abstraction,
  and a statistical query)
• Qualitative impression and quantitative (time)
  measures noted

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The Preliminary-Evaluation Results

• All users answered all queries within 3 minutes
  6 of 7 users completed all three tasks within 90
  seconds
• All users expressed enthusiasm and found the
  interface useful
• Striking redundancy noted in use of interface At
  least four different paths were found to the same
  answers, and five different patterns of use of
  the exploration operators
• Difficult to compare to manual tools, since these
  do not support any automated abstraction or
  explanation of such
• Additional evaluation using a newer interface, a
  limited distributed architecture, and control
  groups given the same data as Excel spreadsheets
  and as paper tables confirmed the results
• Evaluation of the fully distributed KNAVE-II
  architecture using similar electronic- and
  paper-chart control groups is under way

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SummaryAbstraction and Visualization of
Time-Oriented Medical Data

• Temporal abstraction of time-oriented clinical
  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
• Interactive query, visualization, and exploration
  requires runtime access to the domains
  temporal-abstraction ontology
• The visualization and exploration semantics are
  specific to the temporal-abstraction task, but
  not to any particular medical domain