Dr. Fateme Nateghi Haredasht, Stanford University, United States.
Dr. Fateme Nateghi Haredasht is a distinguished postdoctoral researcher at the Stanford Center for Biomedical Informatics Research, where she works under the mentorship of Dr. Jonathan H. Chen. She has a robust background in biomedical sciences, biomedical engineering, and electrical engineering, combined with extensive interdisciplinary experience across institutions in the USA, Belgium, and Iran. Dr. Nateghi’s research merges Artificial Intelligence with healthcare, specializing in medical informatics, survival analysis, and explainable AI. Her work has been published in top-tier journals and presented at prestigious international conferences. With a consistent academic record and multiple honors, she is recognized for her leadership in predictive modeling, particularly in acute kidney injury, treatment retention, and transplantation outcomes. Fateme has played key roles in grant-winning interdisciplinary projects like the 2024 Stanford Bio-X Seed Grant. Her dedication to advancing AI in clinical settings underscores her impact on digital health and personalized medicine.
Profile
Education
Fateme Nateghi Haredasht holds a Ph.D. in Biomedical Sciences from KU Leuven, Belgium, with a research focus on developing predictive models for acute kidney injury in critically ill patients. She earned her M.Sc. in Biomedical Engineering (Bioinformatics) from Amirkabir University of Technology, Tehran. Her undergraduate degree is in Electrical Engineering from the University of Guilan, Iran, where she ranked third among her peers. Throughout her academic journey, Fateme received numerous awards and scholarships, including direct candidacy for graduate programs and national merit-based grants. Her doctoral work, supervised by Prof. Celine Vens and a multidisciplinary medical team, combined machine learning with clinical medicine to improve outcomes for vulnerable patient populations. Her academic foundation in signal processing, programming, and statistics equips her with a unique blend of engineering precision and biomedical insight.
Experience
Dr. Nateghi currently serves as a postdoctoral researcher at Stanford University (2023–present), leading AI-driven research projects in medical informatics under Dr. Jonathan H. Chen. Prior to this, she held a postdoctoral position at KU Leuven’s Department of Public Health and Primary Care, supervised by Prof. Celine Vens. At KU Leuven, she contributed to major research in survival prediction, time-to-event modeling, and donor-recipient matching post-transplantation. She has supervised several master’s and medical students, guiding projects on Alzheimer’s disease and survival analysis. Her teaching portfolio includes TA roles in courses like Bioinformatics and Statistical Analysis at KU Leuven and AmirKabir University. Fateme has also presented her work internationally at conferences in the USA, France, and Iran. Her deep commitment to interdisciplinary research, combined with technical proficiency in Python, R, and LaTeX, allows her to bridge clinical insights and computational innovations, pushing forward digital medicine and evidence-based healthcare.
Research Interests
Dr. Nateghi’s research interests lie at the intersection of artificial intelligence and healthcare. She is passionate about developing explainable AI tools and predictive models to support clinical decision-making, particularly in high-stakes environments like intensive care units. Her current focus includes retrieval-augmented language models, survival analysis, and time-to-event modeling for patient outcome prediction. She has extensively worked on acute kidney injury, pediatric heart transplantation, and substance use disorder retention. Through her research, she seeks to create clinically applicable, ethically grounded AI systems that are interpretable, fair, and integrated with electronic health records. At Stanford, she is exploring how digital consultations can enhance specialty care through generative AI. Her broader aim is to bridge the gap between computational innovation and real-world healthcare delivery, ensuring that machine learning advances lead to tangible improvements in patient outcomes and health equity.
Awards and Honors
Fateme has received numerous accolades throughout her academic career. At Amirkabir University of Technology, she ranked first in her M.Sc. cohort and was recognized as the top student in Bioinformatics. At the undergraduate level, she secured the third position among Electrical Engineering students at the University of Guilan. She was awarded a four-year entrance scholarship for academic excellence and was granted direct M.Sc. admission due to her exceptional performance. In 2024, she served as the lead postdoctoral researcher and primary author on the award-winning proposal for the Stanford Bio-X Interdisciplinary Seed Grant, in collaboration with top researchers from Stanford University. She also received a travel grant to attend the prestigious Cambridge Ellis Machine Learning Summer School in the UK. These honors reflect her commitment to academic rigor, leadership in interdisciplinary projects, and consistent contributions to AI-driven advancements in healthcare.
Publications Top Notes
Validated risk prediction models for outcomes of acute kidney injury: a systematic review
BMC Nephrology (2023) – 17 citations
Comparison between cystatin C- and creatinine-based estimated glomerular filtration rate in the follow-up of patients recovering from a stage-3 AKI in ICU
Journal of Clinical Medicine (2022) –15 citations
Predicting outcomes of acute kidney injury in critically ill patients using machine learning
Scientific Reports (2023) – 13 citations
Supervised fuzzy partitioning
Pattern Recognition (2020) – 10 citations
Clinical entity augmented retrieval for clinical information extraction
npj Digital Medicine (2025) – 9 citations
The effect of different consensus definitions on diagnosing acute kidney injury events and their association with in-hospital mortality
Journal of Nephrology (2022) – 9 citations
Red teaming large language models in medicine: real-world insights on model behavior
medRxiv (2024) –8 citations
Exploiting censored information in self-training for time-to-event prediction
IEEE Access (2023) – 6 citations
Predicting survival outcomes in the presence of unlabeled data
Machine Learning (2022) – 6 citations
Red teaming ChatGPT in medicine to yield real-world insights on model behavior
npj Digital Medicine (2025) – 4 citations
Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate
Scientific Reports (2024) – 4 citations
Predictability of buprenorphine‐naloxone treatment retention: A multi‐site analysis combining electronic health records and machine learning
Addiction (2024) – 3 citations
MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
arXiv preprint (2025) – 2 citations
Conclusion
Dr. Fateme Nateghi Haredasht stands as a compelling candidate for recognition as a leading researcher at the intersection of AI and healthcare. Her interdisciplinary academic background, combined with a strong portfolio of publications, grants, and international collaborations, reflects her capability to solve complex biomedical problems using cutting-edge computational techniques. Her contributions—ranging from predictive analytics in acute kidney injury to retrieval-augmented clinical models—demonstrate a practical yet innovative approach to AI in medicine. She has also played vital roles in mentorship, policy contributions, and curriculum support, proving her dedication to academic and community development. Currently working at Stanford University, she continues to influence the field with her pioneering research and leadership. With a vision centered on ethical, explainable, and impactful AI, Dr. Nateghi exemplifies the qualities of a future scientific leader whose work can redefine how medicine and technology come together to improve patient care and system-wide healthcare efficiency.