Mining a clinical data warehouse to discover disease-finding associations using co-occurrence statistics.
This paper applies co-occurrence statistics to discover disease-finding associations in a clinical data warehouse. We used two methods, chi2 statistics and the proportion confidence interval (PCI) method, to measure the dependence of pairs of diseases and findings, and then used heuristic cutoff values for association selection. An intrinsic evaluation showed that 94 percent of disease-finding associations obtained by chi2 statistics and 76.8 percent obtained by the PCI method were true associations. The selected associations were used to construct knowledge bases of disease-finding relations (KB-chi2, KB-PCI). An extrinsic evaluation showed that both KB-chi2 and KB-PCI could assist in eliminating clinically non-informative and redundant findings from problem lists generated by our automated problem list summarization system.
Cao
Hui
H
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Markatou
Marianthi
M
Melton
Genevieve B
GB
Chiang
Michael F
MF
Hripcsak
George
G
D016009:Chi-Square Distribution; D019295:Computational Biology; D016001:Confidence Intervals; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016247:Information Storage and Retrieval; D051188:Knowledge Bases; D016347:Medical Records Systems, Computerized; D008501:Medical Records, Problem-Oriented; D013223:Statistics as Topic