| Methods, tools and technologies |
 IDAN
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Development of a distributed temporal-abstraction mediator for medical databases
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| keywords |
Main objective |
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Temporal data, time-oriented patient data, temporal abstractions,
dynamic query,
distributed software, medical databases,
database mediator
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Development of IDAN, a distributed
temporal-abstraction database mediator that integrates clinical knowledge and time-oriented
patient data.
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| Summary
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IDAN integrates
multiple time-oriented data sources (including patient
data), domain-specific knowledge sources,
and computation services.
The software supports access to
the appropriate distributed component of a clinical information system to
answer abstract, time-oriented queries when a guideline application is being executed.
Many clinical tasks require a great deal of [time-oriented] patient data of multiple
types to be measured and captured for interpretation, often using electronic media.
This is particularly true in the management of patients with chronic conditions.
Diagnostic or therapeutic decisions depend on
context sensitive interpretation of these data. Most stored data include a time
stamp at which a particular datum is valid. Temporal trends and patterns in clinical
data add significant insights to static analysis. Thus it is desirable automatically to
create abstractions (short, informative, and context-sensitive interpretations*) of
time-oriented clinical data, and to be able to answer queries about these abstractions.
The provision of this capability would benefit both a physician and a decision
support tool (e.g., for patient management, quality assessment and clinical research).
To be of optimum use, a summary should not only use time points
such as dates when data were collected; it should also be capable of aggregating significant
features over intervals of time.
* Abstractions are context-sensitive interpretations generated from time-stamped data.
The task of creating abstractions from
time-tamped raw data is called temporal abstraction.
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IDAN architecture includes the following components and services:
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ALMA Temporal Abstraction Server (the core component of IDAN architecture).
ALMA uses Shahar’s Knowledge-Based Temporal-Abstraction method for reasoning about clinical patient data (see references below).
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KNAVE II- a software tool that facilitates clinical data analysis, visualization, explanation and interactive exploration of large data sets.
Clinical users submit time-oriented queries via the tool.
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Temporal Abstraction Knowledge Acquisition Tool (based on the Protégé ontology editor from Stanford University).
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Medical Vocabularies Search engines:
Loinc search engine
ICD/9 search engine
CPT search engine
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Temporal Abstraction Visual Query Specification
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Local Clinical Database Terms Converter
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| references
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Boaz D, Shahar Y.
Idan: A Distributed Temporal-Abstraction Mediator for Medical Databases.
Proceedings of the 9th Conference on Artificial Intelligence in Medicine—Europe
(AIME) ‘03, pp. 21-30, Protaras, Cyprus, Oct. 2003.
[BGU]
[]
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Abstract "
Many clinical domains involve the collection of different types and
substantial numbers of data over time. This is especially true when monitoring
chronic patients. It is highly desirable to assist users (e.g., clinicians, researchers),
or decision support applications (e.g., diagnosis, therapy, quality assessment),
who need to interpret large amounts of time-oriented data by providing
a useful method for querying not only raw data, but also its abstractions. A
temporal-abstraction database mediator is a modular approach designed for
answering abstract, time-oriented queries. Our approach focuses on the integration
of multiple time-oriented data sources, domain-specific knowledge sources,
and computation services. The mediator mediates abstract time-oriented queries
from any application to the appropriate distributed components that can answer
these queries. We describe a highly modular, distributed implementation
of the temporal database mediator architecture in the medical domain, and provide
insights regarding the effective implementation and application of such an
architecture.
" |
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Shahar Y.
A framework for knowledge-based temporal abstraction.
Artificial Intelligence, Volume 90, Issues 1-2, February 1997, Pages 79-133.
[Elsevier]
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"
A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge.
The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed.
The knowledge-based temporal abstraction method has been implemented in the RÉSUMÉ system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.
"
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Nguyen JH, Shahar Y, Tu SW, Das AK, Musen MA.
A temporal database mediator for protocol-based decision support.
Proc AMIA Annu Fall Symp. 1997;:298-302.
[PubMed]
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"
To meet the data-processing requirements for protocol-based decision support, a clinical data-management system must be capable of creating high-level summaries of time-oriented patient data, and of retrieving those summaries in a temporally meaningful fashion. We previously described a temporal-abstraction module (RESUME) and a temporal-querying module (Chronus) that can be used together to perform these tasks. These modules had to be coordinated by individual applications, however, to resolve the temporal queries of protocol planners. In this paper, we present a new module that integrates the previous two modules and that provides for their coordination automatically. The new module can be used as a standalone system for retrieving both primitive and abstracted time-oriented data, or can be embedded in a larger computational framework for protocol-based reasoning.
"
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Stein A, Musen MA, Shahar Y.
Knowledge acquisition for temporal abstraction.
Proc AMIA Annu Fall Symp. 1996;:204-8.
[PubMed]
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"
Temporal abstraction is the task of detecting relevant patterns in data over time. The knowledge-based temporal-abstraction method uses knowledge about a clinical domain's contexts, external events, and parameters to create meaningful interval-based abstractions from raw time-stamped clinical data. In this paper, we describe the acquisition and maintenance of domain-specific temporal-abstraction knowledge. Using the PROTEGE-II framework, we have designed a graphical tool for acquiring temporal knowledge directly from expert physicians, maintaining the knowledge in a sharable form, and converting the knowledge into a suitable format for use by an appropriate problem-solving method. In initial tests, the tool offered significant gains in our ability to rapidly acquire temporal knowledge and to use that knowledge to perform automated temporal reasoning.
"
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| start date
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end date
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location |
support |
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February 2001
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December 2004 |
Medical Informatics Research Center, Ben Gurion Umiversity, Israel |
Supported in part by NIH award No. LM-06806 |
| contact |
Website |
Yuval Shahar
yshahar@bgumail.bgu.ac.il
David Boaz
dboaz@bgumail.bgu.ac.il
Department of Information Systems Engineering
Ben-Gurion University of the Negev
Beer-Sheva 84105, ISRAEL
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http://medinfo.ise.bgu.ac.il/medlab/
ResearchProjects/RP_Idan.htm
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Entry in directory: January 13 2004
Last main update: January 13 2004 |
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KNAVE-II project (OC)