Lei Clifton

4.7k total citations · 1 hit paper
72 papers, 2.8k citations indexed

About

Lei Clifton is a scholar working on Surgery, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Lei Clifton has authored 72 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Surgery, 19 papers in Artificial Intelligence and 12 papers in Biomedical Engineering. Recurrent topics in Lei Clifton's work include Healthcare Technology and Patient Monitoring (12 papers), Non-Invasive Vital Sign Monitoring (11 papers) and Hemodynamic Monitoring and Therapy (8 papers). Lei Clifton is often cited by papers focused on Healthcare Technology and Patient Monitoring (12 papers), Non-Invasive Vital Sign Monitoring (11 papers) and Hemodynamic Monitoring and Therapy (8 papers). Lei Clifton collaborates with scholars based in United Kingdom, United States and China. Lei Clifton's co-authors include David A. Clifton, Lionel Tarassenko, Marco A. F. Pimentel, Peter Watkinson, Jennifer A. Collister, Xiaonan Liu, Achim Schweikard, Robert Dürichen, Thomas J. Littlejohns and David A. Clifton and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and European Heart Journal.

In The Last Decade

Lei Clifton

66 papers receiving 2.7k 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.

A review of novelty detection

2014 997 citations David A. Clifton, Lei Clifton et al. profile →

Peers

Lei Clifton

CSE

EPIDE

CNC

Marco A. F. Pimentel United Kingdom

Zhengping Che China

Jonathan Weber France

Finale Doshi‐Velez United States

Dinh Phung Australia

Roohallah Alizadehsani Australia

José Luis Rojo‐Álvarez Spain

Sanjay Purushotham United States

Giuseppe De Pietro Italy

Mihail Popescu United States

Marco A. F. Pimentel United Kingdom

Lei Clifton

1.1k ×1.0

271 ×0.9

CSE

469 ×1.6

EPIDE

850 ×3.2

259 ×1.0

CNC

Citations per year, relative to Lei Clifton Lei Clifton (= 1×) peers Marco A. F. Pimentel

Countries citing papers authored by Lei Clifton

Since Specialization

Citations

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

Fields of papers citing papers by Lei Clifton

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lei Clifton

This figure shows the co-authorship network connecting the top 25 collaborators of Lei Clifton. A scholar is included among the top collaborators of Lei Clifton 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 Lei Clifton. Lei Clifton 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

Clifton, Lei, et al.. (2025). Implementation framework for AI deployment at scale in healthcare systems. iScience. 28(5). 112406–112406. 3 indexed citations

Butler, Christopher, et al.. (2025). Dysregulated immune proteins in plasma in the UK Biobank predict multiple myeloma 12 years before clinical diagnosis. Blood Advances. 9(15). 3766–3770.

Gu, Xiao, Jianing Qiu, Lei Lu, et al.. (2025). Transforming label-efficient decoding of healthcare wearables with self-supervised learning and “embedded” medical domain expertise. Communications Engineering. 4(1). 135–135.

Chauhan, Vinod Kumar, Lei Clifton, Huiqi Lu, et al.. (2025). Sample Selection Bias in Machine Learning for Healthcare. 6(4). 1–24. 1 indexed citations

Chauhan, Vinod Kumar, Devendra Singh Dhami, Boyan Gao, et al.. (2025). Beyond Correlations: The Necessity and the Challenges of Causal AI. 1 indexed citations

Liu, Fenglin, Hongjian Zhou, Yining Hua, et al.. (2025). Application of large language models in medicine. Nature Reviews Bioengineering. 3(6). 445–464. 12 indexed citations

Kouchaki, Samaneh, et al.. (2024). Exploring the effectiveness of instruction tuning in biomedical language processing. Artificial Intelligence in Medicine. 158. 103007–103007. 6 indexed citations

Collister, Jennifer A., et al.. (2024). Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank. Cancer Epidemiology Biomarkers & Prevention. 33(6). 812–820. 5 indexed citations

Lü, Lei, Tingting Zhu, Xinshao Wang, et al.. (2024). AutoNet-Generated Deep Layer-Wise Convex Networks for ECG Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(10). 6542–6558.

10.

Collister, Jennifer A., et al.. (2023). Independent external validation of the QRISK3 cardiovascular disease risk prediction model using UK Biobank. Heart. 109(22). 1690–1697. 20 indexed citations

11.

Morelli, Davide, et al.. (2023). Combining machine learning with Cox models to identify predictors for incident post-menopausal breast cancer in the UK Biobank. Scientific Reports. 13(1). 9221–9221. 10 indexed citations

12.

Collister, Jennifer A., et al.. (2023). Polygenic Risk of Prediabetes, Undiagnosed Diabetes, and Incident Type 2 Diabetes Stratified by Diabetes Risk Factors. Journal of the Endocrine Society. 7(4). bvad020–bvad020. 7 indexed citations

13.

Frighi, Valeria, Margaret Smith, Lei Clifton, et al.. (2022). Incidence of fractures in people with intellectual disabilities over the life course: a retrospective matched cohort study. EClinicalMedicine. 52. 101656–101656. 5 indexed citations

14.

Shamout, Farah E., Tingting Zhu, Lei Clifton, et al.. (2019). Early warning score adjusted for age to predict the composite outcome of mortality, cardiac arrest or unplanned intensive care unit admission using observational vital-sign data: a multicentre development and validation. BMJ Open. 9(11). e033301–e033301. 20 indexed citations

15.

Mackillop, Lucy, Jane E. Hirst, Katy Bartlett, et al.. (2018). Comparing the Efficacy of a Mobile Phone-Based Blood Glucose Management System With Standard Clinic Care in Women With Gestational Diabetes: Randomized Controlled Trial. JMIR mhealth and uhealth. 6(3). e71–e71. 124 indexed citations

16.

Lawrence, Vanessa, Lei Clifton, Alan Thomas, et al.. (2017). Feasibility of a staff training and support programme to improve pain assessment and management in people with dementia living in care homes. International Journal of Geriatric Psychiatry. 33(1). 221–231. 17 indexed citations

17.

Clifton, Lei, et al.. (2015). A non-invasive method for estimating lung function. Oxford University Research Archive (ORA) (University of Oxford). 5(3). 2–5. 1 indexed citations

18.

Clifton, David A., Lei Clifton, Gary B. Smith, et al.. (2015). ‘Errors’ and omissions in paper-based early warning scores: the association with changes in vital signs—a database analysis. BMJ Open. 5(7). e007376–e007376. 50 indexed citations

19.

Clifton, David A., et al.. (2012). Vital-sign data fusion models for post-operative patients. Oxford University Research Archive (ORA) (University of Oxford). 410–413. 1 indexed citations

20.

Clifton, Lei, David A. Clifton, Peter Watkinson, & Lionel Tarassenko. (2011). Identification of patient deterioration in vital-sign data using one-class support vector machines. Oxford University Research Archive (ORA) (University of Oxford). 125–131. 42 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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