Anomaly and Signature Filtering Improve Classifier Performance For Detection Of Suspicious Access To EHRs.

TitleAnomaly and Signature Filtering Improve Classifier Performance For Detection Of Suspicious Access To EHRs.
Publication TypeConference Proceedings
Year of Publication2011
AuthorsKim, J, Grillo, JM, Boxwala, AA, Jiang, X, Mandelbaum, RB, Patel, BA, Mikels, D, Vinterbo, SA, Ohno-Machado, L
Conference NameAMIA Annu Symp Proc
Volume2011
Pagination723-31
Date Published2011
ISBN1942-597X
iDASH CategoryPrivacy Technology
AbstractOur 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.
PubMed ID22195129

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