Representing association classification rules mined from health data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3683 LNAI, Issue , 2005, Pages 1225-1231

  Chen, Jie ^a   He, Hongxing ^a   Li, Jiuyong ^d   Jin, Huidong ^a   McAullay, Damien ^a   Williams, Graham ^a,b   Sparks, Ross ^a   Kelman, Chris ^c  

^a CSIRO   (Australia)

^b Australian Taxation Office Canberra   (Australia)

^c The Australian National University   (Australia)

^d UNIVERSITY OF SOUTHERN QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DRUG PRODUCTS; PROBABILITY; RISK ASSESSMENT; TREES (MATHEMATICS);

ASSOCIATION CLASSIFICATION ALGORITHM; DRUG REACTIONS; HEALTH DATA; MEDICAL PRACTITIONERS;

DATA MINING;

EID: 33745292794     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11553939_170     Document Type: Conference Paper

References (6)

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2. Harvey J. Murff, Vimla L. Patel, George Hripcsak, and David W. Bates. Detecting adverse events for patient safety research: a review of current methodologies. Journal of Biomedical Informatics, 36(1/2):131-143, 2003.
3. M. Reid, B. Euerle, and M. Bollinger. Angioedema, 2002. http://www.emedicine.com/med/topic135.htm.
4. J. Li, H. Shen, and R. Topor. Mining the optimal class association rule set. Knowledge-Based Systems, 15(7):399-405, 2002.
5. Stephen C. Newman. Biostatistical Methods in Epidemiology. John Wiley & Sons, July 2001.
6. Graham Williams, Hongxing He, Jie Chen, Huidong Jin, Damien McAullay, Ross Sparks, Jisheng Cui, Simon Hawkins, and Chris Kelman. QLDS: Adverse drug reaction detection towards automation. Technical Report CMIS 04/91, CSIRO Mathematical and Information Sciences, Canberra, 2004.