title: Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs.
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.
authors.affiliation: Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
authors.forename: Jihoon
authors.initials: J
authors.lastname: Kim
authors.forename: Janice M
authors.initials: JM
authors.lastname: Grillo
authors.forename: Aziz A
authors.initials: AA
authors.lastname: Boxwala
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Jiang
authors.forename: Rose B
authors.initials: RB
authors.lastname: Mandelbaum
authors.forename: Bhakti A
authors.initials: BA
authors.lastname: Patel
authors.forename: Debra
authors.initials: D
authors.lastname: Mikels
authors.forename: Staal A
authors.initials: SA
authors.lastname: Vinterbo
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D001185:Artificial Intelligence; D016494:Computer Security; D057286:Electronic Health Records; D006801:Humans; D016015:Logistic Models; D018907:Privacy; D012680:Sensitivity and Specificity