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Found 7 papers, 1 papers with code
Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database
We further show that the transfer learning approach based on the BAN produces models that are better than those trained on just a single institution's data.
A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report
Government agencies collect and manage a wide range of ever-growing datasets.
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
De-identified EHRs do not adequately address the needs of health systems, as de-identified data are susceptible to re-identification and its volume is also limited.
GOAT: GPU Outsourcing of Deep Learning Training With Asynchronous Probabilistic Integrity Verification Inside Trusted Execution Environment
Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a wide range of applications ranging from self-driving cars to COVID-19 treatment discovery.
A Semantic Cover Approach for Topic Modeling
We introduce a novel topic modeling approach based on constructing a semantic set cover for clusters of similar documents.
Get Your Workload in Order: Game Theoretic Prioritization of Database Auditing
For enhancing the privacy protections of databases, where the increasing amount of detailed personal data is stored and processed, multiple mechanisms have been developed, such as audit logging and alert triggers, which notify administrators about suspicious activities; however, the two main limitations in common are: 1) the volume of such alerts is often substantially greater than the capabilities of resource-constrained organizations, and 2) strategic attackers may disguise their actions or carefully choosing which records they touch, making incompetent the statistical detection models.
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Access to electronic health record (EHR) data has motivated computational advances in medical research.
