Dianbo Liu

6.5k total citations · 1 hit paper
33 papers, 1.1k citations indexed

About

Dianbo Liu is a scholar working on Artificial Intelligence, Health Information Management and Health Informatics. According to data from OpenAlex, Dianbo Liu has authored 33 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 5 papers in Health Information Management and 5 papers in Health Informatics. Recurrent topics in Dianbo Liu's work include Machine Learning in Healthcare (7 papers), Privacy-Preserving Technologies in Data (5 papers) and Artificial Intelligence in Healthcare and Education (5 papers). Dianbo Liu is often cited by papers focused on Machine Learning in Healthcare (7 papers), Privacy-Preserving Technologies in Data (5 papers) and Artificial Intelligence in Healthcare and Education (5 papers). Dianbo Liu collaborates with scholars based in United States, China and Canada. Dianbo Liu's co-authors include Hao Deng, Huang Li, Mauricio Santillana, Canelle Poirier, Kenneth D. Mandl, Yifeng Yin, Shifa Zhang, Wei Luo, Dmitriy Dligach and Timothy A. Miller and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Dianbo Liu

30 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

2019 288 citations Huang Li, Hao Deng et al. Journal of Biomedical Informatics profile →

Peers

Countries citing papers authored by Dianbo Liu

Since Specialization

Citations

This map shows the geographic impact of Dianbo Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dianbo Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dianbo Liu more than expected).

Fields of papers citing papers by Dianbo Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dianbo Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dianbo Liu. The network helps show where Dianbo Liu may publish in the future.

Co-authorship network of co-authors of Dianbo Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Dianbo Liu. A scholar is included among the top collaborators of Dianbo Liu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dianbo Liu. Dianbo Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting 2025 ·npj Digital Medicine ·He Zhu, Jun Bai, (unknown), Xiaoxiao Li, Dianbo Liu, David L. Buckeridge, Yue Li	3
2	Spatio-temporal heterogeneity in the international trade resilience during COVID-19 2023 ·Applied Geography ·Wei Luo, Linfeng He, Zihui Yang, (unknown), Yong Wang, Dianbo Liu, Sheng Hu, Li He, Jizhe Xia, Min Chen	8
3	Natural Language Processing Methods to Empirically Explore Social Contexts and Needs in Cancer Patient Notes 2023 ·JCO Clinical Cancer Informatics ·(unknown), Marco Guevara-Vega, Shan Chen, Shalini Moningi, David Kozono, Dianbo Liu, Timothy A. Miller, Guergana Savova, Raymond H. Mak, Danielle S. Bitterman	5
4	Machine learning approaches to predicting no-shows in pediatric medical appointment 2022 ·npj Digital Medicine ·Dianbo Liu, Won-Yong Shin, (unknown), Kathleen Conroy, (unknown), (unknown), Mauricio Santillana	15
5	Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence 2022 ·Journal of Biomedical Informatics ·Dianbo Liu, Kathe Fox, Griffin M. Weber, Tim Miller	14
6	Patients dispensed medications with actionable pharmacogenomic biomarkers: rates and characteristics 2021 ·Genetics in Medicine ·Dianbo Liu, Karen L. Olson, Shannon Manzi, Kenneth D. Mandl	9
7	FeARH: Federated machine learning with anonymous random hybridization on electronic medical records 2021 ·Journal of Biomedical Informatics ·(unknown), (unknown), Hao Deng, (unknown), Dianbo Liu	30
8	Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation 2020 ·JMIR Formative Research ·(unknown), Hongyu He, Ziwei Chen, (unknown), Dianbo Liu	7
9	Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models 2020 ·Journal of Medical Internet Research ·Dianbo Liu, Leonardo Clemente, Canelle Poirier, (unknown), Matteo Chinazzi, Jessica T. Davis, Alessandro Vespignani, Mauricio Santillana	28
10	Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models 2020 ·Journal of Medical Internet Research ·Dianbo Liu, Leonardo Clemente, Canelle Poirier, (unknown), Matteo Chinazzi, Jessica T. Davis, Alessandro Vespignani, Mauricio Santillana	44
11	The role of environmental factors on transmission rates of the COVID-19 outbreak: an initial assessment in two spatial scales 2020 ·Scientific Reports ·Canelle Poirier, Wei Luo, Maimuna S. Majumder, Dianbo Liu, Kenneth D. Mandl, Todd A. Mooring, Mauricio Santillana	149
12	LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data 2020 ·PLoS ONE ·Huang Li, Yifeng Yin, (unknown), Shifa Zhang, Hao Deng, Dianbo Liu	104
13	Using Artificial Neural Network Condensation to Facilitate Adaptation of Machine Learning in Medical Settings by Reducing Computational Burden: Model Design and Evaluation Study 2020 ·JMIR Formative Research ·Dianbo Liu, Ming Zheng, Nestor A. Sepulveda	4
14	Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization 2019 ·Nucleic Acids Research ·Dianbo Liu, José Dávila-Velderrain, Zhizhuo Zhang, Manolis Kellis	2
15	Two-stage Federated Phenotyping and Patient Representation Learning 2019 ·PubMed ·Dianbo Liu, Dmitriy Dligach, Timothy A. Miller	65
16	Current state of science in machine learning methods for automatic infant pain evaluation using facial expression information: study protocol of a systematic review and meta-analysis 2019 ·BMJ Open ·Dan Cheng, Dianbo Liu, Lisa Philpotts, Dana P. Turner, Timothy T. Houle, Lucy Chen, Miaomiao Zhang, Jianjun Yang, Wei Zhang, Hao Deng	6
17	Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records breakdown → 2019 ·Journal of Biomedical Informatics ·Huang Li, (unknown), (unknown), (unknown), Hao Deng, Dianbo Liu	288
18	LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data 2018 ·arXiv (Cornell University) ·Huang Li, Yifeng Yin, (unknown), Shifa Zhang, Hao Deng, Dianbo Liu	34
19	DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain 2017 ·International Joint Conference on Artificial Intelligence ·Dianbo Liu, (unknown), (unknown), Ognjen Rudovic, Rosalind W. Picard	11
20	Universal attenuators and their interactions with feedback loops in gene regulatory networks 2017 ·Nucleic Acids Research ·Dianbo Liu, Luca Albergante, T. J. Newman	3

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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