<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kim, Jihoon</style></author><author><style face="normal" font="default" size="100%">Grillo, Janice M</style></author><author><style face="normal" font="default" size="100%">Boxwala, Aziz A</style></author><author><style face="normal" font="default" size="100%">Jiang, Xiaoqian</style></author><author><style face="normal" font="default" size="100%">Mandelbaum, Rose B</style></author><author><style face="normal" font="default" size="100%">Patel, Bhakti A</style></author><author><style face="normal" font="default" size="100%">Mikels, Debra</style></author><author><style face="normal" font="default" size="100%">Vinterbo, Staal A</style></author><author><style face="normal" font="default" size="100%">Ohno-Machado, Lucila</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Anomaly and Signature Filtering Improve Classifier Performance For Detection Of Suspicious Access To EHRs.</style></title><secondary-title><style face="normal" font="default" size="100%">AMIA Annu Symp Proc</style></secondary-title><alt-title><style face="normal" font="default" size="100%">AMIA Annu Symp Proc</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Privacy Technology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">2011</style></volume><pages><style face="normal" font="default" size="100%">723-31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10(-6). Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful.</style></abstract></record></records></xml>