Assistant Professor, at Mashhad University of Medical Sciences, Iran📖
Dr. Toktam Dehghani is a skilled educator and researcher specializing in medical informatics and bioinformatics. With a Ph.D. in Computer Engineering, she has extensive experience in applying artificial intelligence and data mining techniques to various fields of healthcare, particularly in diagnostics and predictive modeling. Dr. Dehghani is deeply involved in cutting-edge research on genetic disorders, cancer detection, and AI-based health technology. She has developed several AI-driven platforms and decision support systems that are shaping the future of personalized medicine and healthcare
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Education Background🎓
Dr. Toktam Dehghani holds a Ph.D. in Computer Science from the University of Manchester, UK, where she specialized in artificial intelligence and its applications in medical data analysis. Prior to her doctoral studies, she earned her Master’s degree in Bioinformatics from the University of Tehran, Iran. During her academic journey, she gained expertise in bioinformatics, medical data analysis, and the application of machine learning techniques to healthcare problems. Dr. Dehghani’s academic background reflects her strong foundation in both computer science and biomedical research, equipping her with a unique interdisciplinary perspective for solving complex health-related challenges through innovative technologies.
Dr. Toktam Dehghani is an Assistant Professor at the Medical Informatics Department of Mashhad University of Medical Sciences. She lectures postgraduate students in Artificial Intelligence (AI), Medical Software Development, and Bioinformatics. With over a decade of experience in academia, she has also served as a lecturer at Ferdowsi University of Mashhad and Toos Higher Education Institute, where she taught courses in Artificial Intelligence, Data Mining, Bioinformatics, and Advanced Algorithms to undergraduate and postgraduate students. Dr. Dehghani is also the Manager of the Health Technology Incubator at SMARTDX Co., leading the development of AI-based platforms for diagnosing genetic disorders and cancers
Research Interests🔬
Dr. Dehghani’s research interests lie at the intersection of Machine Learning, Bioinformatics, and Medical Informatics. She is particularly focused on the application of AI and data mining techniques to solve complex problems in genetic disorders, cancer diagnosis, and healthcare decision support systems. Her recent research includes predictive models for medical student performance, cardiovascular event prediction, pulmonary thromboembolism diagnosis, and machine learning for genetic data analysis. She has also worked extensively on protein structure prediction and the application of deep learning in bioinformatics.
Author Metrics
Dr. Toktam Dehghani has established herself as a prominent author in the field of computer science and bioinformatics. With over 20 peer-reviewed publications, her work has been cited more than 300 times, highlighting her significant contribution to the academic community. She maintains an h-index of 10, demonstrating her consistent impact on the field. Her research articles have been published in reputable journals such as Bioinformatics, Journal of Medical Systems, and IEEE Transactions on Biomedical Engineering, covering topics like artificial intelligence, machine learning applications in healthcare, and bioinformatics. Dr. Dehghani is recognized for her expertise in utilizing computational methods to address complex biological and medical challenges.
Publications Top Notes 📄
1. Deep Learning on Ultrasound Images of Thyroid Nodules
- Authors: Y Sharifi, MA Bakhshali, T Dehghani, M DanaiAshgzari, M Sargolzaei, et al.
- Journal: Biocybernetics and Biomedical Engineering
- Volume: 44
- Year: 2021
- Summary: This study investigates the application of deep learning techniques on ultrasound images to aid in the detection and diagnosis of thyroid nodules, enhancing diagnostic accuracy.
2. Efficient Semi-Partitioning and Rate-Monotonic Scheduling Hard Real-Time Tasks on Multi-Core Systems
- Authors: M Naghibzadeh, P Neamatollahi, R Ramezani, A Rezaeian, T Dehghani
- Conference: 8th IEEE International Symposium on Industrial Embedded Systems (SIES)
- Year: 2013
- Summary: This paper addresses the problem of scheduling real-time tasks on multi-core systems, focusing on an efficient semi-partitioning method and rate-monotonic scheduling for hard real-time tasks.
3. A Comparative Study of Explainable Ensemble Learning and Logistic Regression for Predicting In-Hospital Mortality in the Emergency Department
- Authors: Z Rahmatinejad, T Dehghani, B Hoseini, F Rahmatinejad, A Lotfata, et al.
- Journal: Scientific Reports
- Volume: 14(1)
- Article Number: 3406
- Year: 2024
- Summary: This paper compares the performance of ensemble learning models with logistic regression for predicting in-hospital mortality, with a focus on the explainability of the models in clinical settings.
4. BetaProbe: A Probability-Based Method for Predicting Beta Sheet Topology Using Integer Programming
- Authors: M Eghdami, T Dehghani, M Naghibzadeh
- Conference: 5th International Conference on Computer and Knowledge Engineering
- Year: 2015
- Summary: BetaProbe presents a method for predicting the beta-sheet topology of proteins, utilizing integer programming for more accurate computational predictions in bioinformatics.
5. Enhancement of Protein β-Sheet Topology Prediction Using Maximum Weight Disjoint Path Cover
- Authors: T Dehghani, M Naghibzadeh, J Sadri
- Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Volume: 16(6)
- Year: 2018
- Summary: This work improves the prediction of β-sheet topology in proteins by using a maximum weight disjoint path cover, contributing to advancements in protein structure prediction.
Conclusion
Dr. Toktam Dehghani is a highly deserving candidate for the Best Researcher Award due to her innovative research in AI, bioinformatics, and healthcare. Her contributions to personalized medicine and AI-driven diagnostic systems have the potential to revolutionize healthcare practices, especially in the areas of genetic disorders and cancer. While there are areas for improvement, such as enhancing clinical integration and expanding the scope of her AI models, her dedication to advancing healthcare through technology positions her as a leader in the field. Dr. Dehghani’s ongoing contributions to both academia and industry ensure that her impact will continue to grow, making her an exemplary choice for this prestigious award.