Attributes for causal inference in electronic healthcare databases

作者: Jenna Reps , Jonathan M. Garibaldi , Uwe Aickelin , Daniele Soria , Jack E. Gibson

DOI: 10.1109/CBMS.2013.6627871

关键词:

摘要: Side effects of prescription drugs present a serious issue. Existing algorithms that detect side generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria could be used by classifying algorithm definitively identify directly. We found it would advantageous use association strength, temporality and specificity criteria.

参考文章(5)
M. Hall, Correlation-based Feature Selection for Machine Learning PhD Thesis, Waikato Univer-sity. ,(1998)
Austin Bradford Hill, THE ENVIRONMENT AND DISEASE: ASSOCIATION OR CAUSATION? Journal of the Royal Society of Medicine. ,vol. 58, pp. 295- 300 ,(1965) , 10.1177/0141076814562718
G. Niklas Norén, Johan Hopstadius, Andrew Bate, Kristina Star, I. Ralph Edwards, Temporal pattern discovery in longitudinal electronic patient records Data Mining and Knowledge Discovery. ,vol. 20, pp. 361- 387 ,(2010) , 10.1007/S10618-009-0152-3
Martijn J. Schuemie, Methods for drug safety signal detection in longitudinal observational databases: LGPS and LEOPARD. Pharmacoepidemiology and Drug Safety. ,vol. 20, pp. 292- 299 ,(2011) , 10.1002/PDS.2051
Christopher L. Sistrom, Cynthia W. Garvan, Proportions, Odds, and Risk Radiology. ,vol. 230, pp. 12- 19 ,(2004) , 10.1148/RADIOL.2301031028