Ke Lin

942 total citations
23 papers, 564 citations indexed

About

Ke Lin is a scholar working on Epidemiology, Infectious Diseases and Artificial Intelligence. According to data from OpenAlex, Ke Lin has authored 23 papers receiving a total of 564 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Epidemiology, 5 papers in Infectious Diseases and 4 papers in Artificial Intelligence. Recurrent topics in Ke Lin's work include Sepsis Diagnosis and Treatment (6 papers), SARS-CoV-2 and COVID-19 Research (4 papers) and Machine Learning in Healthcare (4 papers). Ke Lin is often cited by papers focused on Sepsis Diagnosis and Treatment (6 papers), SARS-CoV-2 and COVID-19 Research (4 papers) and Machine Learning in Healthcare (4 papers). Ke Lin collaborates with scholars based in China, Netherlands and Taiwan. Ke Lin's co-authors include Guilan Kong, Yonghua Hu, Guido Hooiveld, Harrie J. Kools, Arjen Lommen, Ram Kumar Basnet, Anand Gavai, Richard G. F. Visser, Philip J. de Groot and Guusje Bonnema and has published in prestigious journals such as Nature Communications, Frontiers in Immunology and International Journal of Biological Macromolecules.

In The Last Decade

Ke Lin

22 papers receiving 559 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ke Lin China 11 170 137 125 64 59 23 564
Mi Ζhou China 14 114 0.7× 55 0.4× 62 0.5× 55 0.9× 26 0.4× 44 539
Takehiko Oami Japan 13 191 1.1× 38 0.3× 129 1.0× 54 0.8× 54 0.9× 44 490
Yibing Zhu China 14 259 1.5× 51 0.4× 68 0.5× 76 1.2× 19 0.3× 48 555
Xianfei Ding China 17 245 1.4× 55 0.4× 402 3.2× 117 1.8× 116 2.0× 64 1.0k
Xiaoming Xu China 12 43 0.3× 47 0.3× 156 1.2× 102 1.6× 62 1.1× 27 672
Andrew P. Michelson United States 11 140 0.8× 68 0.5× 86 0.7× 56 0.9× 50 0.8× 32 467
Feyissa Challa Ethiopia 10 61 0.4× 42 0.3× 61 0.5× 38 0.6× 72 1.2× 28 437
Arshad Channanath Kuwait 13 63 0.4× 60 0.4× 94 0.8× 82 1.3× 123 2.1× 42 571
Marko Kumrić Croatia 15 72 0.4× 34 0.2× 163 1.3× 143 2.2× 47 0.8× 89 851
Shinn‐Jye Liang Taiwan 18 153 0.9× 33 0.2× 65 0.5× 154 2.4× 46 0.8× 49 744

Countries citing papers authored by Ke Lin

Since Specialization
Citations

This map shows the geographic impact of Ke Lin'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 Ke Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ke Lin more than expected).

Fields of papers citing papers by Ke Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ke Lin. 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 Ke Lin. The network helps show where Ke Lin may publish in the future.

Co-authorship network of co-authors of Ke Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Ke Lin. A scholar is included among the top collaborators of Ke Lin 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 Ke Lin. Ke Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Liu, Zeying, Meijuan Wu, Xiaoying He, et al.. (2025). Presence of SARS-CoV-2 in fetal organs via intraamniotic infection. Nature Communications. 16(1). 10261–10261.
2.
Sun, Yuhan, Jiahuan Lu, Jing Wu, et al.. (2024). Potential mechanism of CARD16 protein action and susceptibility to sepsis in the elderly infected population: Through transcriptome analysis of blood. International Journal of Biological Macromolecules. 281(Pt 4). 136578–136578. 1 indexed citations
3.
Zhang, Yi, Zhangfan Fu, H. C. Zhang, et al.. (2024). Proteomic and Cellular Characterization of Omicron Breakthrough Infections and a Third Homologous or Heterologous Boosting Vaccination in a Longitudinal Cohort. Molecular & Cellular Proteomics. 23(6). 100769–100769. 1 indexed citations
4.
Lin, Ke, Yuanhan Zhao, Bin Xu, et al.. (2023). Clinical Diagnostic Performance of Droplet Digital PCR for Suspected Bloodstream Infections. Microbiology Spectrum. 11(1). e0137822–e0137822. 25 indexed citations
5.
Zhang, H. C., Kun Zhu, Feng Zhu, et al.. (2023). Risk of reinfection and severity with the predominant BA.5 Omicron subvariant China, from December 2022 to January 2023. Emerging Microbes & Infections. 13(1). 2292071–2292071. 8 indexed citations
6.
7.
Fang, Hairui, Shiqi Li, Dong Wang, et al.. (2023). Decoding throat-language using flexibility sensors with machine learning. Sensors and Actuators A Physical. 352. 114192–114192. 10 indexed citations
8.
9.
Lin, Ke, et al.. (2022). The direct application of plasma droplet digital PCR in the ultra-early pathogen detection and warning during sepsis: Case reports. Journal of Infection and Public Health. 15(4). 450–454. 6 indexed citations
10.
Chen, Xiaohua, Hongyu Wang, Jingwen Ai, et al.. (2022). Identification of CKD, bedridden history and cancer as higher-risk comorbidities and their impact on prognosis of hospitalized Omicron patients: a multi-centre cohort study. Emerging Microbes & Infections. 11(1). 2501–2509. 11 indexed citations
11.
Kong, Guilan, Jingyi Wu, Hong Chu, et al.. (2021). Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis–Treated Patients Using Stacked Generalization: Model Development and Validation Study. JMIR Medical Informatics. 9(5). e17886–e17886. 12 indexed citations
12.
Lin, Yu, et al.. (2021). [Prediction of intensive care unit readmission for critically ill patients based on ensemble learning].. PubMed. 53(3). 566–572. 8 indexed citations
13.
Kong, Guilan, Ke Lin, & Yonghua Hu. (2020). Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU. BMC Medical Informatics and Decision Making. 20(1). 251–251. 96 indexed citations
14.
Wu, Jingyi, Yu Lin, Ke Lin, Yonghua Hu, & Guilan Kong. (2020). Predicting length of stay in intensive care unit using ensemble learning methods. 841–848. 1 indexed citations
15.
Lin, Ke, Yonghua Hu, & Guilan Kong. (2019). Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model. International Journal of Medical Informatics. 125. 55–61. 110 indexed citations
16.
Lin, Yu, Chao Yang, Hong Chu, et al.. (2019). Association between the Charlson Comorbidity Index and the risk of 30-day unplanned readmission in patients receiving maintenance dialysis. BMC Nephrology. 20(1). 363–363. 19 indexed citations
17.
Xie, Junqing, Chunxiao Li, Ke Lin, et al.. (2017). A review of modeling methods for predicting in-hospital mortality of patients in intensive care unit. Journal of Emergency and Critical Care Medicine. 1(8). 18–18. 13 indexed citations
18.
Zhang, Guobin, et al.. (2016). Terminal density dependent resource management in cognitive heterogeneous networks. Wireless Networks. 23(5). 1509–1522. 3 indexed citations
19.
Lin, Ke, Harrie J. Kools, Philip J. de Groot, et al.. (2011). MADMAX – Management and analysis database for multiple ~omics experiments. Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics. 8(2). 59–74. 125 indexed citations
20.
Tseng, Chih‐Huang, et al.. (1999). Comparison of immunogenicity of simultaneous and nonsimultaneous vaccination with MMR and JE vaccine among 15-month-old children.. PubMed. 40(3). 161–5. 3 indexed citations

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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