Dianbo Liu

发表

Discrete-Valued Neural Communication in Structured Architectures Enhances Generalization

Alex Lamb, Chen Sun, Dianbo Liu, 2021 .

Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation.

Dianbo Liu, Rulin Shao, Hongyu He, 2020, JMIR formative research.

Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

Alessandro Vespignani, Mauricio Santillana, Dianbo Liu, 2020, Journal of medical Internet research.

Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multi-Center Study During the COVID-19 Outbreak

M. Santillana, D. Tenen, Y. Xiong, 2020 .

Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence

Kathe P. Fox, G. Weber, K. Fox, 2020, J. Biomed. Informatics.

Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models (Preprint)

A. Vespignani, M. Santillana, Matteo Chinazzi, 2020 .

Machine learning approaches to predicting no-shows in pediatric medical appointment

M. Santillana, Dianbo Liu, K. Conroy, 2022, npj Digital Medicine.

Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak

M. Santillana, D. Tenen, Y. Xiong, 2020, Cancer discovery.

Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare data

I. Kohane, J. Smoller, Kun‐Hsing Yu, 2022, ArXiv.

Leyu Dai, Dianbo Liu, He Zhu, 2020, ArXiv.

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, Matteo Chinazzi, 2020, ArXiv.

Patients Dispensed Medications with Actionable Pharmacogenomic Biomarkers: Rates and Characteristics

K. Mandl, S. Manzi, Dianbo Liu, 2021, Genetics in medicine : official journal of the American College of Medical Genetics.

The role of absolute humidity on transmission rates of the COVID-19 outbreak

K. Mandl, M. Santillana, M. Lipsitch, 2020, medRxiv.

Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records

Li Huang, Dianbo Liu, Dianbo Liu, 2019, J. Biomed. Informatics.

Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence pdf

Kenneth D. Mandl, Dianbo Liu, Timothy A Miller, 2019, ArXiv.

The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak: An Initial Assessment in Two Spatial Scales.

K. Mandl, M. Santillana, M. Majumder, 2020, SSRN.

LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

Li Huang, Zeng Fu, Dianbo Liu, 2018, PloS one.

LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data pdf

Hao Deng, Dianbo Liu, Li Huang, 2018, ArXiv.

FeARH: Federated machine learning with anonymous random hybridization on electronic medical records

Dianbo Liu, He Zhu, Ziwei Chen, 2021, J. Biomed. Informatics.

Two-stage Federated Phenotyping and Patient Representation Learning pdf

Dmitriy Dligach, Dianbo Liu, Timothy A. Miller, 2019, BioNLP@ACL.

DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain

Andrew Shea, Rosalind W. Picard, Dianbo Liu, 2017, AffComp@IJCAI.

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records pdf

Dianbo Liu, Tianyi Zhang, Shirui Zhang, 2021, 2022 IEEE International Conference on Big Data (Big Data).

Abstract CT406: Patients with cancer appear more vulnerable to SARS-COV-2: A multi-center study during the COVID-19 outbreak

M. Santillana, D. Tenen, L. Mucci, 2020 .

Natural Language Processing Methods to Empirically Explore Social Contexts and Needs in Cancer Patient Notes.

R. Mak, G. Savova, D. Kozono, 2023, JCO clinical cancer informatics.

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

Tim Miller, Dianbo Liu, Timothy Miller, 2020, ArXiv.

The role of environmental factors on transmission rates of the COVID-19 outbreak: an initial assessment in two spatial scales

K. Mandl, M. Santillana, M. Majumder, 2020, Scientific Reports.

FADL: Federated-Autonomous Deep Learning for Distributed Electronic Health Record pdf

Kenneth D. Mandl, Dianbo Liu, Raheel Sayeed, 2018, ArXiv.

Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation.

Hui Liu, Dianbo Liu, Hongyu He, 2019, JMIR formative research.

Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

Alessandro Vespignani, Mauricio Santillana, Dianbo Liu, 2020, Journal of medical Internet research.

LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

Dianbo Liu, Shifa Zhang, Hao Deng, 2018, PLoS ONE.

Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden pdf

Ming Zheng, Dianbo Liu, Néstor Sepúlveda, 2018, ArXiv.

Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence

Kathe P. Fox, G. Weber, Dianbo Liu, 2022, J. Biomed. Informatics.

Patients dispensed medications with actionable pharmacogenomic biomarkers: rates and characteristics

K. Mandl, S. Manzi, Dianbo Liu, 2021, Genetics in Medicine.