Publications

Plain-language summaries of my research. Full citations link to each paper.

1. Prevalence of 406 rare diseases by ethnicity and their COVID-19 burden — first author #

Gu Q, et al. (on behalf of the CVD-COVID-UK/COVID-IMPACT Consortium). Prevalence of 406 rare diseases by ethnicity and their associated COVID-19 infection burden: a national cross-sectional study of 62.5 million people in England. medRxiv (2026); under revision at Scientific Reports. https://doi.org/10.64898/2026.01.13.26344068

The first national map of how common 406 rare diseases are across 19 ethnic groups in England, from linked records for 62.5 million people — showing that rare diseases, and their COVID-19 burden, fall unevenly across ethnicities.

2. C-reactive protein responses and antibiotic prescribing — first author #

Gu Q, et al. Interplay between C-reactive protein responses and antibiotic prescribing in people with suspected infection. BMC Infectious Diseases 25:987 (2025). https://doi.org/10.1186/s12879-025-11381-9

Across 51,544 suspected-infection episodes, changes in a common inflammation marker (CRP) nudged antibiotic decisions only modestly — most went unchanged — but early CRP movement strongly predicted survival.

3. Transformers and LLMs as efficient feature extractors for EHR studies — joint first author #

Yuan K, Yoon CH, Gu Q, et al. Transformers and large language models are efficient feature extractors for electronic health record studies. Communications Medicine 5:83 (2025). https://doi.org/10.1038/s43856-025-00790-1

Tested whether language models (BERT, GPT) can read free-text antibiotic notes to identify infection type across ~938,000 prescriptions; a fine-tuned clinical BERT reached F1 0.98, and free text captured 31% more detail than diagnostic codes.

4. Predicting individual and hospital-level discharge with machine learning — co-author #

Wei J, … Gu Q, et al. Predicting individual patient and hospital-level discharge using machine learning. Communications Medicine 4:236 (2024). https://doi.org/10.1038/s43856-024-00673-x

Machine-learning models that predict who will be discharged within 24 hours and forecast daily hospital discharge numbers accurately enough (AUROC 0.87) to help with capacity planning.

5. Vital-sign and inflammatory-marker patterns in suspected bloodstream infection — first author #

Gu Q, et al. Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection. Journal of Infection 88:106156 (2024). https://doi.org/10.1016/j.jinf.2024.106156

Mapped how inflammation markers and vital signs typically recover after suspected bloodstream infection across 88,348 episodes, turning them into personalised “recovery charts” — like growth charts — to track whether a patient is on track.

6. Amoxicillin vs co-amoxiclav for community-acquired pneumonia — co-author #

Wei J, … Gu Q, et al. No evidence of difference in mortality with amoxicillin versus co-amoxiclav for hospital treatment of community-acquired pneumonia. Journal of Infection 88:106161 (2024). https://doi.org/10.1016/j.jinf.2024.106161

Using causal-inference methods (propensity-score matching and IPTW) on 16,072 admissions, found no difference in deaths between a narrow- and broad-spectrum antibiotic — supporting wider use of the narrower drug to help curb resistance.

7. Vancomycin dosing guideline and predictive factors — first author #

Gu Q, et al. Assessment of an institutional guideline for vancomycin dosing and identification of predictive factors associated with dose and drug trough levels. Journal of Infection 85:382–389 (2022). https://doi.org/10.1016/j.jinf.2022.06.029

Audited a hospital’s electronic vancomycin dosing guideline in 3,767 patients: it was followed well, but only a quarter reached the target drug level, so I proposed age-, weight-, and kidney-tailored dosing.

8. “Bloodstream infection”: a valuable concept we should keep — second author, correspondence #

Danielsen AS, Gu Q, Fostervold A, Eyre DW, Bjørnholt JV. ‘Bloodstream infection’: a valuable concept we should keep in our toolbox. Journal of Infection (2024). https://doi.org/10.1016/j.jinf.2024.106236

A short correspondence arguing that “bloodstream infection” remains a clinically useful concept worth keeping in the diagnostic toolbox.