Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions

Yuval Shahar, Dina Goren-Bar, David Boaz, Gil Tahan

Research output: Contribution to journalArticlepeer-review

93 Scopus citations


Objectives: We present KNAVE-II, 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. KNAVE-II main contributions to the fields of temporal reasoning and intelligent user interfaces are: (1) the capability for interactive computation and visualization of domain specific temporal abstractions, supported by ALMA - a computational engine that applies the domain knowledge base to the clinical time-oriented database. (2) Semantic (ontology-based) navigation and exploration of the data, knowledge, and temporal abstractions, supported by the IDAN mediator, a distributed architecture that enables runtime access to domain-specific knowledge bases that are maintained by expert physicians. Methods and materials: KNAVE-II was designed according to 12 requirements that were defined through iterative cycles of design and user-centered evaluation. The complete architecture has been implemented and evaluated in a cross-over study design that compared the KNAVE-II module versus two existing methods: paper charts and an Excel electronic spreadsheet. A small group of clinicians answered the same queries, using the domain of oncology and a set of 1000 patients followed after bone-marrow transplantation. Results: The results show that users are able to perform medium to hard difficulty level queries faster and more accurately by using KNAVE-II than paper charts and Excel. Moreover, KNAVE-II was ranked first in preference by all users, along all usability dimensions. Conclusions: Initial evaluation of KNAVE-II and its supporting knowledge based temporal-mediation architecture, by applying it to a large data base of patients monitored several years after bone marrow transplantation (BMT), has produced highly encouraging results.

Original languageEnglish
Pages (from-to)115-135
JournalArtificial Intelligence in Medicine
Issue number2
StatePublished - 1 Oct 2006


  • Clinical systems
  • Human-computer interaction
  • Intelligent visualization
  • Knowledge-based systems
  • Medical informatics
  • Temporal abstraction
  • Temporal reasoning

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence


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