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Conditional anomaly detection methods for patient-management alert systems
Michal Valko, Gregory Cooper, Amy Seybert +3
Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly det…
Learning predictive models for combinations of heterogeneous proteomic data sources
Michal Valko, Richard Pelikan, Miloš Hauskrecht
Multiple technologies that measure expression levels of protein mixtures in the human body offer a potential for detection and understanding the disease. The recent increase of the…
Outlier detection for patient monitoring and alerting
Miloš Hauskrecht, Iyad Batal, Michal Valko +3
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). O…
Conditional outlier detection for clinical alerting
Milos Hauskrecht, Michal Valko, Shyam Visweswaran +3
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) sys…
Feature importance analysis for patient management decisions
Michal Valko, Milos Hauskrecht
The objective of this paper is to understand what characteristics and features of clinical data influence physician's decision about ordering laboratory tests or prescribing medica…
Evidence-based anomaly detection in clinical domains
Milos Hauskrecht, Michal Valko, Branislav Kveton +2
Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic anomaly detection methods that…