102 citations · 297 across the 16 of their papers we have counts for
5 papers · 1 filter
Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving
Rulin Shao, Hongyu He, Hui Liu +1
Artificial neural network has achieved unprecedented success in the medical domain. This success depends on the availability of massive and representative datasets. However, data c…
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning
Rulin Shao, Hui Liu, Dianbo Liu
Artificial neural network has achieved unprecedented success in a wide variety of domains such as classifying, predicting and recognizing objects. This success depends on the avail…
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
Dianbo Liu, Kathe Fox, Griffin Weber +1
Health information is generally fragmented across silos. Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system,…
Two-stage Federated Phenotyping and Patient Representation Learning
Dianbo Liu, Dmitriy Dligach, Timothy Miller
A large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely tim…
Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
Li Huang, Dianbo Liu
Electronic medical records (EMRs) supports the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare even…