| Title | Anomaly and Signature Filtering Improve Classifier Performance For Detection Of Suspicious Access To EHRs. |
| Publication Type | Conference Proceedings |
| Year of Publication | 2011 |
| Authors | Kim, J, Grillo, JM, Boxwala, AA, Jiang, X, Mandelbaum, RB, Patel, BA, Mikels, D, Vinterbo, SA, Ohno-Machado, L |
| Conference Name | AMIA Annu Symp Proc |
| Volume | 2011 |
| Pagination | 723-31 |
| Date Published | 2011 |
| ISBN | 1942-597X |
| Abstract | 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. |
