activity
20172020
most citedA machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

102 citations · 202 across the 9 of their papers we have counts for

collaborators

14 papers

cs.AI2020

FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network

He Zhu, Dianbo Liu

The concept of disinformation is to use fake messages to confuse people in order to protect the real information. This strategy can be adapted into data science to protect valuable…

cs.LG20207 cited

Patient similarity: methods and applications

Leyu Dai, He Zhu, Dianbo Liu

Patient similarity analysis is important in health care applications. It takes patient information such as their electronic medical records and genetic data as input and computes t…

stat.OT2020102 cited

A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

Dianbo Liu, Leonardo Clemente, Canelle Poirier +5

We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably…

cs.CL202024 cited

Federated pretraining and fine tuning of BERT using clinical notes from multiple silos

Dianbo Liu, Tim Miller

Large scale contextual representation models, such as BERT, have significantly advanced natural language processing (NLP) in recently years. However, in certain area like healthcar…

cs.CY2020

Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records

Jianfei Cui, He Zhu, Hao Deng +2

Sometimes electrical medical records are restricted and difficult to centralize for machine learning, which could only be trained in distributed manner that involved many instituti…

cs.LG20199 cited

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…