# Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records

*PRI Rank #5 · Topics and Trends in Most Cited Antimicrobial Resistance in Staphylococcus Papers, Class of 2026*

*Canonical URL: https://pri.pepkio.com/top-papers/antimicrobial-resistance-in-staphylococcus/2026/rank-5*

| Field | Value |
| --- | --- |
| Rank | #5 |
| 18m citations | 25 |
| Journal | Nature Communications |
| Year | 2024 |
| DOI | 10.1038/s41467-024-46211-0 |
| Corresponding authors | Masayuki Nigo |
| Institution | University of Texas Health Science Center at Houston, United States |

**Ranking page:** [Topics and Trends in Most Cited Antimicrobial Resistance in Staphylococcus Papers, Class of 2026](https://pri.pepkio.com/top-papers/antimicrobial-resistance-in-staphylococcus/2026)

**Paper link:** [10.1038/s41467-024-46211-0](https://doi.org/10.1038/s41467-024-46211-0)

## Topics

Methicillin-resistant Staphylococcus aureus (MRSA) · PyTorch_EHR · electronic health record (EHR) time-series data · MRSA culture positivity prediction · deep learning · logistic regression · light gradient boost machine (LGBM) · AUROC · Memorial Hermann Hospital System · Medical Information Mart for Intensive Care (MIMIC)-IV · external validation · risk stratification · antimicrobial therapy optimization · personalized prediction

## Cite this ranking

```
Pepkio Research Index (PRI). Topics and Trends in Most Cited Antimicrobial Resistance in Staphylococcus Papers, Class of 2026. https://pri.pepkio.com/top-papers/antimicrobial-resistance-in-staphylococcus/2026. Accessed 2026-07-22.

Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459
```