Literature DB >> 11604792

RASTA: a distributed temporal abstraction system to facilitate knowledge-driven monitoring of clinical databases.

M J O'Connor1, W E Grosso, S W Tu, M A Musen.   

Abstract

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.

Mesh:

Year:  2001        PMID: 11604792

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  6 in total

1.  The Chronus II temporal database mediator.

Authors:  Martin J O'Connor; Samson W Tu; Mark A Musen
Journal:  Proc AMIA Symp       Date:  2002

2.  An analytic framework fo space-time aberrancy detection in public health surveillance data.

Authors:  David L Buckeridge; Mark A Musen; Paul Switzer; Monica Crubézy
Journal:  AMIA Annu Symp Proc       Date:  2003

3.  A dynamic distributed architecture for temporal data abstraction.

Authors:  Vijay P Chauhan; Martin J O'connor; Amar K Das
Journal:  AMIA Annu Symp Proc       Date:  2006

4.  Incorporating Knowledge-Driven Insights into a Collaborative Filtering Model to Facilitate the Differential Diagnosis of Rare Diseases.

Authors:  Feichen Shen; Hongfang Liu
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

5.  A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data: An Approach for Building Effective Clinical Decision-Making System.

Authors:  Nancy Yesudhas Jane; Khanna Harichandran Nehemiah; Kannan Arputharaj
Journal:  Appl Clin Inform       Date:  2016-01-13       Impact factor: 2.342

6.  PROTEMPA: a method for specifying and identifying temporal sequences in retrospective data for patient selection.

Authors:  Andrew R Post; James H Harrison
Journal:  J Am Med Inform Assoc       Date:  2007-06-28       Impact factor: 4.497

  6 in total

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