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Cited by 146Artificial Intelligence in HealthcareHealthcare Technology and Patient MonitoringMachine Learning in Healthcare

Temporal abstraction in intelligent clinical data analysis: A survey

Stacey, Michael, McGregor, Carolyn·2007·Artificial Intelligence in Medicine
DOI 10.1016/j.artmed.2006.08.002
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Abstract

Objective Intelligent clinical data analysis systems require precise qualitative descriptions of data to enable effective and context sensitive interpretation to take place. Temporal abstraction (TA) provides the means to achieve such descriptions, which can then be used as input to a reasoning engine where they are evaluated against a knowledge base to arrive at possible clinical hypotheses. This paper surveys previous research into the development of intelligent clinical data analysis systems that incorporate TA mechanisms and presents research synergies and trends across the research reviewed, especially those associated with the multi-dimensional nature of real-time patient data streams. The motivation for this survey is case study based research into the development of an intelligent real-time, high-frequency patient monitoring system to provide detection of temporal patterns within multiple patient data streams.Results The survey was based on factors that are of importance to broaden research into temporal abstraction and on characteristics we believe will assume an increasing level of importance for future clinical IDA systems. These factors were: aspects of the data that is abstracted such as source domain and sample frequency, complexity available within abstracted patterns, dimensionality of the TA and data environment and the knowledge and reasoning underpinning TA processes.Conclusion It is evident from the review that for intelligent clinical data analysis systems to progress into the future where clinical environments are becoming increasingly data-intensive, the ability for managing multi-dimensional aspects of data at high observation and sample frequencies must be provided. Also, the detection of complex patterns within patient data requires higher levels of TA than are presently available. The conflicting matters of computational tractability and temporal reasoning within a real-time environment present a non-trivial problem for investigation in regard to these matters. Finally, to be able to fully exploit the value of learning new knowledge from stored clinical data through data mining and enable its application to data abstraction, the fusion of data mining and TA processes becomes a necessity.

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  • Most home or remote monitoring applications deal predominantly with prediction and anomaly detection, whereas clinical applications typically focus on diagnosis.

    “Based on the literature, most monitoring applications which consider home settings or remote monitoring deal predominantly with prediction and anomaly detection whereas the applications in clinical settings are typically focused on diagnosis [10,21].”

    From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [21]

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Authors
Stacey, M., & McGregor, C.
Affiliations
Western Sydney University (AU)
Corresponding author
Stacey, Michael
ORCID
McGregor, Carolyn
Venue
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Published
2007-01
Volume
39
Issue
1
Pages
1-24
ISSN
0933-3657 (ISSN-L)
DOI
10.1016/j.artmed.2006.08.002
OpenAlex
W2122874925
Cited by
146
References
156
Citations by year
2025: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 6 · 2019: 4 · 2018: 4 · 2017: 9 · 2016: 8 · 2015: 15 · 2014: 16 · 2013: 19 · 2012: 5
FWCI
20.46
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Artificial Intelligence in Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
temporal abstraction, clinical reasoning, patient monitoring, high-frequency data, temporal pattern detection, multidimensional data, temporal reasoning, computational tractability, clinical knowledge base, data mining
MeSH terms
Artificial Intelligence, Data Collection, Information Storage and Retrieval

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