Laila Rasmy

1.4k total citations · 1 hit paper
16 papers, 736 citations indexed

About

Laila Rasmy is a scholar working on Artificial Intelligence, Infectious Diseases and Health Information Management. According to data from OpenAlex, Laila Rasmy has authored 16 papers receiving a total of 736 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 4 papers in Infectious Diseases and 3 papers in Health Information Management. Recurrent topics in Laila Rasmy's work include Machine Learning in Healthcare (11 papers), Topic Modeling (4 papers) and Artificial Intelligence in Healthcare (3 papers). Laila Rasmy is often cited by papers focused on Machine Learning in Healthcare (11 papers), Topic Modeling (4 papers) and Artificial Intelligence in Healthcare (3 papers). Laila Rasmy collaborates with scholars based in United States, China and Serbia. Laila Rasmy's co-authors include Degui Zhi, Ziqian Xie, Cui Tao, Yang Xiang, Hua Xu, W. Jim Zheng, Yujia Zhou, Yonghui Wu, Masayuki Nigo and Hulin Wu and has published in prestigious journals such as Nature Communications, Journal of Medical Internet Research and Remote Sensing.

In The Last Decade

Laila Rasmy

16 papers receiving 715 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.

Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

2021 453 citations Laila Rasmy, Yang Xiang et al. npj Digital Medicine profile →

Peers

Laila Rasmy

HIM

RNMI

Sunyang Fu United States

Yikuan Li United States

Hanyin Wang United States

Andrew Wen United States

Tanja Magoč United States

Saeed Mehrabi United States

Chaitanya Shivade United States

Sungrim Moon United States

Beau Norgeot United States

Valentina Tibollo Italy

Sunyang Fu United States

Laila Rasmy

521 ×1.0

139 ×0.7

HIM

259 ×2.2

233 ×2.3

115 ×1.3

RNMI

Citations per year, relative to Laila Rasmy Laila Rasmy (= 1×) peers Sunyang Fu

Countries citing papers authored by Laila Rasmy

Since Specialization

Citations

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

Fields of papers citing papers by Laila Rasmy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laila Rasmy

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

All Works

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16 of 16 papers shown

Rasmy, Laila, et al.. (2025). Advancing Pancreatic Cancer Prediction with a Next Visit Token Prediction Head on Top of Med-BERT. Cancers. 17(3). 516–516. 2 indexed citations

Zhi, Degui, et al.. (2024). Foundation Models, Generative AI, and Large Language Models. CIN Computers Informatics Nursing. 42(5). 377–387. 5 indexed citations

Nigo, Masayuki, et al.. (2024). Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records. Nature Communications. 15(1). 2036–2036. 24 indexed citations

Li, Rongbin, Yujia Zhou, Laila Rasmy, et al.. (2023). Prediction of Brain Metastases Development in Patients With Lung Cancer by Explainable Artificial Intelligence From Electronic Health Records. JCO Clinical Cancer Informatics. 7(7). e2200141–e2200141. 6 indexed citations

Xie, Ziqian, et al.. (2023). A Deep-Learning-based Two-Compartment Predictive Model (PKRNN-2CM) for Vancomycin Therapeutic Drug Monitoring. 484–484. 2 indexed citations

Zhang, Shenghan, Ruixiang Tang, Sirui Ding, et al.. (2023). PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data. 268–275. 2 indexed citations

Nigo, Masayuki, et al.. (2022). PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data. Journal of Biomedical Informatics. 133. 104166–104166. 17 indexed citations

Rasmy, Laila, Masayuki Nigo, Bijun S. Kannadath, et al.. (2022). Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data. The Lancet Digital Health. 4(6). e415–e425. 40 indexed citations

Rasmy, Laila, et al.. (2021). Automatic Sub-Pixel Co-Registration of Remote Sensing Images Using Phase Correlation and Harris Detector. Remote Sensing. 13(12). 2314–2314. 15 indexed citations

10.

Rasmy, Laila, Yang Xiang, Ziqian Xie, Cui Tao, & Degui Zhi. (2021). Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. npj Digital Medicine. 4(1). 86–86. 453 indexed citations breakdown →

11.

Nigo, Masayuki, Laila Rasmy, Sarah May, et al.. (2021). Real World Long-term Assessment of The Efficacy of Tocilizumab in Patients with COVID-19: Results From A Large De-identified Multicenter Electronic Health Record Dataset in the United States. International Journal of Infectious Diseases. 113. 148–154. 2 indexed citations

12.

Xiang, Yang, Hangyu Ji, Yujia Zhou, et al.. (2020). Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study. Journal of Medical Internet Research. 22(7). e16981–e16981. 35 indexed citations

13.

Rasmy, Laila, Yujia Zhou, Yang Xiang, et al.. (2020). Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies. Journal of the American Medical Informatics Association. 27(10). 1593–1599. 16 indexed citations

14.

Williams, George W., Laila Rasmy, Xueying Wang, et al.. (2020). Vasopressor treatment and mortality following nontraumatic subarachnoid hemorrhage: a nationwide electronic health record analysis. Neurosurgical FOCUS. 48(5). E4–E4. 16 indexed citations

15.

Xiang, Yang, Jun Xu, Yuqi Si, et al.. (2019). Time-sensitive clinical concept embeddings learned from large electronic health records. BMC Medical Informatics and Decision Making. 19(S2). 58–58. 24 indexed citations

16.

Rasmy, Laila, Yonghui Wu, Xin Geng, et al.. (2018). A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. Journal of Biomedical Informatics. 84. 11–16. 77 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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