102 citations · 297 across the 16 of their papers we have counts for
16 papers
Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare data
Dianbo Liu, Karmel W. Choi, Paulo Lizano +4
Importance: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3% of the whole population. The ability to conduct risk screening of SMIs at larg…
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records
Tianyi Zhang, Shirui Zhang, Ziwei Chen +1
Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communication technology develops rapidly and an unp…
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…
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…
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…
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…
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…
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…
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden
Dianbo Liu, Nestor Sepulveda, Ming Zheng
Machine Learning (ML) applications on healthcare can have a great impact on people's lives helping deliver better and timely treatment to those in need. At the same time, medical d…
FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
Dianbo Liu, Timothy Miller, Raheel Sayeed +1
Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to se…
LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
Li Huang, Yifeng Yin, Zeng Fu +3
Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many differen…
DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain
Dianbo Liu, Fengjiao Peng, Andrew Shea +3
Previous research on automatic pain estimation from facial expressions has focused primarily on "one-size-fits-all" metrics (such as PSPI). In this work, we focus on directly estim…