Learn about the flu shot, COVID-19 vaccine, and our masking policy »
New to MyHealth?
Manage Your Care From Anywhere.
Access your health information from any device with MyHealth. You can message your clinic, view lab results, schedule an appointment, and pay your bill.
ALREADY HAVE AN ACCESS CODE?
DON'T HAVE AN ACCESS CODE?
NEED MORE DETAILS?
MyHealth for Mobile
Get the iPhone MyHealth app »
Get the Android MyHealth app »
Abstract
Many biological databases contain a large number of variables, among which events of interest may be very infrequent. Using a single data mining method to analyze such databases may not find adequate predictors. The HIV Drug Resistance Database at Stanford University stores sequential HIV-1 genotype-test results on patients taking antiretroviral drugs. We have analyzed the infrequent event of gene mutation changes by combining three data mining methods. We first use association rule analysis to scan through the database and identify potentially interesting mutation patterns with relatively high frequency. Next, we use logistic regression and classification trees to further investigate these patterns by analyzing the relationship between treatment history and mutation changes. Although the AUC measures of the overall prediction is not very high, our approach can effectively identify strong predictors of mutation change and thus focus the analytic efforts of researchers in verifying these results.
View details for PubMedID 17369658