title: Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions.
abstract: OBJECTIVE
  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
  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.
authors.affiliation: Department of Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105, Israel. yshahar@bgumail.bgu.ac.il
authors.forename: Yuval
authors.initials: Y
authors.lastname: Shahar
authors.forename: Dina
authors.initials: D
authors.lastname: Goren-Bar
authors.forename: David
authors.initials: D
authors.lastname: Boaz
authors.forename: Gil
authors.initials: G
authors.lastname: Tahan
mesh_terms: D001185:Artificial Intelligence; D016026:Bone Marrow Transplantation; D002985:Clinical Protocols / Q000592:standards; D004341:Drug Evaluation / Q000706:statistics & numerical data; D006801:Humans; D012680:Sensitivity and Specificity; D013997:Time Factors; D014584:User-Computer Interface