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image of Laila Rasmy Bekhet, PhD

Laila Rasmy Bekhet, PhD

Assistant Professor

Department of Health Data Science and Artificial Intelligence


[email protected] | 713-500-3644

Laila Rasmy Bekhet joined McWilliams School of Biomedical Informatics at UTHealth Houston, formerly UTHealth Houston School of Biomedical Informatics (SBMI) in January 2022 as an assistant professor after earning her PhD in Biomedical Informatics from the University of Texas Health Science Center at Houston. She holds a bachelor’s in pharmaceutical sciences, a master’s in business administration, and a master’s in biomedical informatics. During her PhD, she was a predoctoral research fellow with the UTHealth Innovation in Cancer Prevention Research for the Cancer Prevention Research Institute of Texas. Similar to her interdisciplinary educational background, Laila had a diverse working experience. She started as a clinical pharmacist at the National Cancer Institute in Egypt and ended up as a principal consultant helping pharmaceutical manufacturers across the borders to comply with the global regulatory standards for computerized systems, before coming back to graduate school.

Laila’s research focus is on developing implementable AI algorithms, mostly utilizing cutting-edge deep learning techniques and big clinical data sources. She is interested in building solutions that can bridge the gap between health data science research and real-world practice. One of her current research activities is to train a large clinical foundation model using the structured clinical data for more than 50 million patients. “Following the success of foundation models in the NLP domain and our earlier Med-BERT, such a clinical foundation model can boost the performance of a wide range of clinical predictive models, and hopefully offer a plausible solution for common issues that hurdle the acceptance of deep learning-based models in practice,” Laila said.

Laila has published research articles in top journals in the biomedical informatics domain including Lancet Digital Health, Nature (npj) Digital medicine, JAMIA, and JBI, as well as clinical journals such as JNS Neurosurgical focus and the International Journal of Infectious Diseases. She is also a senior member of the Healthcare Information and Management Systems Society (HIMSS), and a member of the American Medical Informatics Association (AMIA), the Society for Health Economics and Outcomes Research (ISPOR), and the International Society of Pharmaceutical Engineering (ISPE). She participates in several special interest groups and has served as a reviewer for several peer-reviewed journals and conferences.


  • PhD, Biomedical Informatics, University of Texas Health Science Center, 2021
  • MS, Biomedical Informatics, University of Texas Health Science Center, 2017
  • MBA, Business Administration, Maastricht School of Management, Netherlands, 2011
  • BSc, Pharmaceutical Sciences, Ain Shams University, Egypt, 2002

Areas of Expertise

  • Deep Learning
  • Predictive Modeling
  • Biomedical Data Mining
  • Implementation Science and Project Management
  • GxP compliance
  • Pharmaceutical Manufacturing
  • Clinical Pharmacy

Staff Support

 Leticia Flores | 713-500-3912

Selected Recent Publications:

  1. Rasmy L, Nigo M, Kannadath BS, Xie Z, Mao B, Patel K, Zhou Y, Zhang W, Ross A, Xu H, Zhi D. CovRNN—A recurrent neural network model for predicting outcomes of COVID-19 patients: model development and validation using EHR data. Lancet Digital Health. (accepted Feb 2022)

  2. Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digital Medicine. 2021 May 20;4(1):86. doi: 10.1038/s41746-021-00455-y. PMID: 34017034; PMCID: PMC8137882.

  3. Rasmy L, Tiryaki F, Zhou Y, Xiang Y, Tao C, Xu H, Zhi D.  Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies. Journal of the American Medical Informatics Association. 2020 Oct 1;27(10):1593-1599. doi: 10.1093/jamia/ocaa180. PMID: 32930711; PMCID: PMC7647355. (Featured article on UMLS@30 AMIA special issue)

  4. Williams G (co-first), Maorify V (co-first), Rasmy L (co-first), Brown D, Yu D, Zhu H, Talebi Y, Wang X, Thomas E, Zhu G, Yaseen A, Zhi D, Aguilar D, Wu H. Vasopressor treatment and mortality following nontraumatic subarachnoid hemorrhage: a nationwide electronic health record analysis. Neurosurgical Focus. 2020;48(5):E4. doi: 10.3171/2020.2.FOCUS191002. PMID: 32357322.

  5. Rasmy L, Wu Y, Wang N, Geng X, Zheng WJ, Wang F, Wu H, Xu H, Zhi D. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR dataset. Journal of Biomedical Informatics. 2018 Aug;84:11-16. doi: 10.1016/j.jbi.2018.06.011. Epub 2018 Jun 15. PMID: 29908902; PMCID: PMC6076336.

  6. Nigo M, Rasmy L, May S, Rao A, Karimaghaei S, Mao B, Kannadath B, Hoz A, Arias C, Li L, Zhu D. Real world assessment of the efficacy of tocilizumab in patients with COVID19: results from a large de-identified multicenter electronic health record dataset in the United States. International Journal of Infectious Diseases: IJID : official publication of the International Society for Infectious Diseases, vol. 113 148-154. 29 Sep. 2021, doi:10.1016/j.ijid.2021.09.067.

  7. Shahidi N, Lin X, Munarko Y, Rasmy L, Ngo T. AQUA: An Advanced QUery Architecture for the SPARC Portal. F1000Research.

  8. Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, Wu ST, Zheng WJ, Xu H, Zhi D, Zhang Y, Tao C. Asthma exacerbation prediction and risk factor analysis based on a time-sensitive, attentive neural network: retrospective cohort study. Journal of Medical Internet Research. 2020 Jul 31;22(7):e16981. doi: 10.2196/16981. PMID: 32735224; PMCID: PMC7428917.

  9. Xiang Y, Xu J, Si Y, Li Z, Rasmy L, Zhou Y, Tiryaki F, Li F, Zhang Y, Wu Y, Jiang X. Time-sensitive clinical concept embeddings learned from large electronic health records. BMC Medical Informatics and Decision Making. 2019 Apr 9;19(Suppl 2):58. doi: 10.1186/s12911-019-0766-3. PMID: 30961579; PMCID: PMC6454598.