Medical data mining on the internet: research on a cancer information system

Andrea L. Houston; Hsinchun Chen; Susan M. Hubbard; Bruce R. Schatz; Tobun D. Ng; Robin R. Sewell; Kristin M. Tolle

doi:10.1023/a:1006548623067

Medical data mining on the internet: research on a cancer information system

Andrea L. Houston, Hsinchun Chen, Susan M. Hubbard, Bruce R. Schatz, Tobun D. Ng, Robin R. Sewell, Kristin M. Tolle

Research output: Contribution to journal › Article › peer-review

31 Scopus citations

Abstract

This paper discusses several data mining algorithms and techniques that we have developed at the University of Arizona Artificial Intelligence Lab. We have implemented these algorithms and techniques into several prototypes, one of which focuses on medical information developed in cooperation with the National Cancer Institute (NCI) and the University of Illinois at Urbana-Champaign. We propose an architecture for medical knowledge information systems that will permit data mining across several medical information sources and discuss a suite of data mining tools that we are developing to assist NCI in improving public access to and use of their existing vast cancer information collections.

Original language	English (US)
Pages (from-to)	437-466
Number of pages	30
Journal	Artificial Intelligence Review
Volume	13
Issue number	5
DOIs	https://doi.org/10.1023/a:1006548623067
State	Published - 1999

ASJC Scopus subject areas

Language and Linguistics
Linguistics and Language
Artificial Intelligence

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10.1023/a:1006548623067

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title = "Medical data mining on the internet: research on a cancer information system",

abstract = "This paper discusses several data mining algorithms and techniques that we have developed at the University of Arizona Artificial Intelligence Lab. We have implemented these algorithms and techniques into several prototypes, one of which focuses on medical information developed in cooperation with the National Cancer Institute (NCI) and the University of Illinois at Urbana-Champaign. We propose an architecture for medical knowledge information systems that will permit data mining across several medical information sources and discuss a suite of data mining tools that we are developing to assist NCI in improving public access to and use of their existing vast cancer information collections.",

author = "Houston, {Andrea L.} and Hsinchun Chen and Hubbard, {Susan M.} and Schatz, {Bruce R.} and Ng, {Tobun D.} and Sewell, {Robin R.} and Tolle, {Kristin M.}",

note = "Funding Information: This project was funded primarily by a grant from the National Cancer Institute (NCI) “Information Analysis and Visualization for Cancer Literature” (1996–1997), a grant from National Library of Medicine (NLM), “Semantic Retrieval for Toxicology and Hazardous Substance Databases” (1996–1997), an NSF/CISE “Intelligent Internet Categorization and Search” project (1995– 1998), and the NSF/ARPA/NASA Illinois Digital Library Initiative project, “Building the Interspace” (1994–1998).",

year = "1999",

doi = "10.1023/a:1006548623067",

language = "English (US)",

volume = "13",

pages = "437--466",

journal = "Artificial Intelligence Review",

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TY - JOUR

T1 - Medical data mining on the internet

T2 - research on a cancer information system

AU - Houston, Andrea L.

AU - Chen, Hsinchun

AU - Hubbard, Susan M.

AU - Schatz, Bruce R.

AU - Ng, Tobun D.

AU - Sewell, Robin R.

AU - Tolle, Kristin M.

N1 - Funding Information: This project was funded primarily by a grant from the National Cancer Institute (NCI) “Information Analysis and Visualization for Cancer Literature” (1996–1997), a grant from National Library of Medicine (NLM), “Semantic Retrieval for Toxicology and Hazardous Substance Databases” (1996–1997), an NSF/CISE “Intelligent Internet Categorization and Search” project (1995– 1998), and the NSF/ARPA/NASA Illinois Digital Library Initiative project, “Building the Interspace” (1994–1998).

PY - 1999

Y1 - 1999

N2 - This paper discusses several data mining algorithms and techniques that we have developed at the University of Arizona Artificial Intelligence Lab. We have implemented these algorithms and techniques into several prototypes, one of which focuses on medical information developed in cooperation with the National Cancer Institute (NCI) and the University of Illinois at Urbana-Champaign. We propose an architecture for medical knowledge information systems that will permit data mining across several medical information sources and discuss a suite of data mining tools that we are developing to assist NCI in improving public access to and use of their existing vast cancer information collections.

AB - This paper discusses several data mining algorithms and techniques that we have developed at the University of Arizona Artificial Intelligence Lab. We have implemented these algorithms and techniques into several prototypes, one of which focuses on medical information developed in cooperation with the National Cancer Institute (NCI) and the University of Illinois at Urbana-Champaign. We propose an architecture for medical knowledge information systems that will permit data mining across several medical information sources and discuss a suite of data mining tools that we are developing to assist NCI in improving public access to and use of their existing vast cancer information collections.

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JF - Artificial Intelligence Review

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Medical data mining on the internet: research on a cancer information system

Abstract

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