Laila Rasmy Bekhet joined 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的研究beplay苹果手机能用吗重点是开发可实施的AI算法，主要是利用最先进的深度学习技术和大型临床数据来源。她有兴趣建立可以弥合数据科学研究与现实世界实践之间差距的解决方案。beplay苹果手机能用吗她目前的研究活动之一是使用超过5000beplay苹果手机能用吗万患者的结构化临床数据培训大型临床基础模型。“继基础模型在NLP领域和我们较早的Med-Bert中取得成功之后，这种临床基础模型可以提高广泛的临床预测模型的性能，并希望为常见问题提供合理的解决方案，以刺激接受接受实践中基于深度学习的模型。” Laila说。
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.
laila.rasmy.gindybekhet@uth.tmc.edu
Phone: 713-500-3644
Leticia弗洛
Phone: 713-500-3912
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.柳叶刀数字医疗。(accepted2022年2月）
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.
Rasmy L，Tiryaki F，Zhou Y，Xiang Y，Tao C，Xu H，ZhiD。EHR数据的表示为预测建模：UMLS与其他术语之间的比较。美国医学信息学协会杂志。2020 Oct 1;27(10):1593-1599. doi: 10.1093/jamia/ocaa180. PMID: 32930711; PMCID: PMC7647355.(Featured article on UMLS@30 AMIA special issue)
Williams G（联合首先），Maorify V（联合第一），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.
Rasmy L，Wu Y，Wang N，Geng X，Zheng WJ，Wang F，Wu H，Xu H，ZhiD。使用大型且异构的EHR数据集对基于复发性神经网络的预测模型的概括性研究。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.
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.国际传染病杂志：IJID：国际传染病学会的官方出版, vol. 113 148-154. 29 Sep. 2021, doi:10.1016/j.ijid.2021.09.067.
Shahidi N，Lin X，Munarko Y，Rasmy L, Ngo T. AQUA: An Advanced QUery Architecture for the SPARC Portal。F1000Research.
Xiang Y, Ji H, Zhou Y, Li F, Du J,Rasmy L，Wu ST，Zheng WJ，Xu H，Zhi D，Zhang Y，TaoC。基于时间敏感的，细心的神经网络的哮喘加剧预测和危险因素分析：回顾性队列研究。Journal of Medical Internet Research.2020 Jul 31;22(7):e16981. doi: 10.2196/16981. PMID: 32735224; PMCID: PMC7428917.
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年4月9日； 19（增刊2）：58。doi：10.1186/s12911-019-0766-3。PMID：30961579;PMCID：PMC6454598。