most citedOutlier detection for patient monitoring and alerting

141 citations · 238 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG202614 cited

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…

cs.LG2026141 cited

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…

cs.LG202648 cited

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…

cs.LG202635 cited

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…

eess.IV2024

MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images

Yanwu Xu, Li Sun, Wei Peng +7

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly…