Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions (original) (raw)
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2003
Interpretation and exploration of longitudinal clinical data is a major part of diagnosis, therapy, quality assessment, and clinical research, particularly for chronic patients. KNAVE-II is an intelligent interface to a distributed architecture specific to the tasks of query, knowledge-based interpretation, summarization, visualization, interactive exploration of large numbers of distributed time-oriented clinical data and dynamic sensitivity analysis of these data. The web-based architecture enables users (e.g., physicians) to query, visualize and explore clinical time-oriented databases. Both, the generation of context-sensitive interpretations (abstractions) of the time-stamped data, as well as the dynamic visual exploration of the raw data and the multiple levels of concepts abstracted from these data, are supported by runtime access to domain-specific knowledge bases, maintained by domain experts. KNAVE-II was designed according to a set of well-defined desiderata. The architec...
Navigation and visualization of abstractions of time-oriented clinical data
1997
We describe the methodology and architecture of a knowledge-based, interactive visualization system that enables physicians and medical support personnel to draw conclusions from heterogeneous time-oriented clinical data. Our system employs domain-specific ontologies to produce temporal and statistical abstractions of data, and also as the basis for semantically-based browsing and visualization. This builds on previous work in data mining, temporal reasoning, and information visualization, but offers fundamental advantages over any isolated approach, by leveraging each off the others. We performed an evaluation of a prototype, leading us to conclude that users can indeed use the system to perform such semantically-based browsing in a reasonable amount of time.
Intelligent visualization and exploration of time-oriented clinical data
Topics in health information management, 1999
Physicians and other care providers often need to quickly browse and interpret large numbers of time-oriented clinical data. Reducing the information overload involving such tasks is a major goal for medical information systems. We describe a conceptual architecture and software implementation specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration of time-oriented clinical data and the multiple levels of meaningful concepts that can be derived from these data. We build on our work on abstraction of time-oriented clinical data using a knowledge base, acquired from expert physicians, of temporal properties of the data. The core module of the new framework is called KNAVE (Knowledge-based Navigation of Abstractions for Visualization and Explanation). Health care providers can manipulate the display though several visualization and exploration operators. These operators have semantics that are domain independent but that are custom...
A framework for distributed mediation of temporal-abstraction queries to clinical databases
Artificial Intelligence in Medicine, 2005
Objective: The specification and creation of a distributed system that integrates medical knowledge bases with time-oriented clinical databases; the goal is to answer complex temporal queries regarding both raw data and its abstractions, such as are often required in medical applications. Methods: (1) Specification, design, and implementation of a generalized access method to a set of heterogeneous clinical data sources, by using a virtual medical-record interface and by mapping the local terms to a set of standardized medical vocabularies; (2) specification of a generalized interface to a set of knowledge sources; (3) specification and implementation of a service, called ALMA that computes complex time-oriented medical queries that include both raw data and abstractions derivable from it; (4) design and implementation of a mediator, called IDAN, that answers raw-data and abstract queries by integrating the appropriate clinical data with the relevant medical knowledge and uses the computation service to answer the queries; (5) an expressive language that enables definition of time-dependent medical queries, which are referred to the mediator; (6) evaluation of the effect of the system, when combined with a new visual interface, called KNAVE-II, on the speed and accuracy of answering a set of complex queries in an oncology sub domain, by a group of clinicians, compared to answering these queries using paper or an electronic spreadsheet. Results: We have implemented the full IDAN architecture. The IDAN/KNAVE-II combination significantly increased the accuracy and speed of answering complex queries about both the data and their abstractions, compared to the standard tools. Conclusion: The implemented architecture proves the feasibility of the distributed integration of medical knowledge sources with clinical data of heterogeneous sources. The results suggest that the proposed IDAN modular architecture has potential significance for supporting the automation of clinical tasks such as diagnosis, monitoring, therapy, and quality assessment. #
A framework for intelligent visualization of multiple time-oriented medical records
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2005
Management of patients, especially chronic patients, requires presentation and processing of very large amounts of time-oriented clinical data. Using regular means such as text or tables is often ineffective, thus we propose to use the visual presentation of the information in decision support, especially in the medical domain. Displaying only raw data is not sufficient, because it still requires the user to derive meaningful conclusions from large amount of data. In order to support the computation process, we provide automated mechanisms for temporal abstraction. These mechanisms perform derivation of context-specific, interval-based abstract concepts from raw time-stamped clinical data, by using a domain-specific knowledge base. Then, these abstractions can be visualized and explored. In addition, in many cases (e.g. when comparing the effect of new drugs on various groups of patients) a view of multiple records is more effective than a view of each indi-vidual record separately....
Studies in health technology and informatics, 2001
The time dimension is very important for applications that reason with clinical data. Unfortunately, this task is inherently computationally expensive. As clinical decision support systems tackle increasingly varied problems, they will increase the demands on the temporal reasoning component, which may lead to slow response times. This paper addresses this problem. It describes a temporal reasoning system called RASTA that uses a distributed algorithm that enables it to deal with large data sets. The algorithm also supports a variety of configuration options, enabling RASTA to deal with a range of application requirements.
Applying temporal abstraction in medical information systems
2003
Physicians and medical decision-support applications, such as for diagnosis, therapy, monitoring, quality assessment, and clinical research, reason about patients in terms of abstract, clinically meaningful concepts, typically over significant time periods. Clinical databases, however, store only raw, time-stamped data. Thus, there is a need to bridge this gap. We introduce the Temporal Abstraction Language (TAR) which enables specification of abstract relations involving raw data and abstract concepts, and use it for defining typical medical abstraction patterns. For each pattern we further analyze finiteness properties of the answer set. The TAR language is implemented as the reasoning module in a practical diagnosis system. Index Terms-temporal reasoning, temporal databases, temporal query languages, knowledge-based systems and knowledge representation, medical informatics and temporal abstraction.