A multidimensional temporal abstractive data mining framework

Authors:
Heidi Bjering;Carolyn McGregor
Affiliations:
University of Western Sydney, Penrith South DC NSW, Australia;University of Western Sydney, Penrith South DC NSW, Australia and University of Ontario Institute of Technology (UOIT), Oshawa ON, Canada
Venue:
HIKM '10 Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management - Volume 108
Year:
2010

Citing 10
Cited 0

A framework for knowledge-based temporal abstraction

Artificial Intelligence
CAPSUL: A constraint-based specification of repeating patterns in time-oriented data

Annals of Mathematics and Artificial Intelligence
A Survey of Temporal Knowledge Discovery Paradigms and Methods

IEEE Transactions on Knowledge and Data Engineering
Temporal reasoning for decision support in medicine

Artificial Intelligence in Medicine
Temporal abstraction in intelligent clinical data analysis: A survey

Artificial Intelligence in Medicine
Learning recurrent behaviors from heterogeneous multivariate time-series

Artificial Intelligence in Medicine
Data mining with Temporal Abstractions: learning rules from time series

Data Mining and Knowledge Discovery
Temporal data mining for the quality assessment of hemodialysis services

Artificial Intelligence in Medicine
A framework for distributed mediation of temporal-abstraction queries to clinical databases

Artificial Intelligence in Medicine
Implementing an integrated time-series data mining environment based on temporal pattern extraction methods: a case study of an interferon therapy risk mining for chronic hepatitis

JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a framework to support analysis and trend detection in historical data from Neonatal Intensive Care Unit (NICU) patients. The clinical research extensions contribute to fundamental data mining framework research through the integration of temporal abstraction and support of null hypothesis testing within the data mining processes. The application of this new data mining approach is the analysis of level shifts and trends in historical temporal data and to cross correlate data mining findings across multiple data streams for multiple neonatal intensive care patients in an attempt to discover new hypotheses indicative of the onset of some condition. These hypotheses can then be evaluated and defined as rules to be applied in the monitoring of neonates in real-time to enable early detection of possible onset of conditions. This can assist in faster decision making which in turn may avoid conditions developing into serious problems where treatment may be futile.