Bio

As a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research (BMIR), I find myself at the exciting intersection of machine learning and healthcare. My journey began with a PhD in Biomedical Sciences from KU Leuven in Belgium, where I explored the complexities of machine learning algorithms and their transformative potential in clinical settings. My research focused on adapting these algorithms for time-to-event data, a method used to predict when specific events may occur in a patient’s future.

At Stanford, my work centers on building trustworthy AI systems to enhance healthcare delivery. I develop and evaluate machine learning models that integrate structured electronic health records (EHRs) and unstructured clinical notes to support real-world clinical decision-making. My recent projects include predicting treatment retention in opioid use disorder, improving antibiotic stewardship for urinary tract infections, and enabling digital consultations through large language models (LLMs). I'm particularly interested in embedding-based retrieval and retrieval-augmented generation (RAG) methods that help bridge cutting-edge AI research with clinical practice.

My role involves not just advancing the integration of machine learning in healthcare, but also collaborating with a diverse team of clinicians, data scientists, and engineers. Together, we're striving to unravel complex healthcare challenges and ultimately improve patient outcomes.

Professional Education

  • PhD, KU Leuven, Biomedical sciences (2023)

Stanford Advisors

Contact

  • Academic
    fnateghi@stanford.edu
    University - Scholar Department: Department of Biomedical Data Science Position: Postdoctoral Scholar

Additional Info

Links

All Publications

  • Session Introduction: AI and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Nateghi Haredasht, F., Kim, D., Romano, J. D., Tison, G., Daneshjou, R., Chen, J. H. 2025; 30: 33-39

    Abstract

    Artificial Intelligence (AI) technologies are increasingly capable of processing complex and multilayered datasets. Innovations in generative AI and deep learning have notably enhanced the extraction of insights from both unstructured texts, images, and structured data alike. These breakthroughs in AI technology have spurred a wave of research in the medical field, leading to the creation of a variety of tools aimed at improving clinical decision-making, patient monitoring, image analysis, and emergency response systems. However, thorough research is essential to fully understand the broader impact and potential consequences of deploying AI within the healthcare sector.

    View details for PubMedID 39670359

  • Clinical entity augmented retrieval for clinical information extraction. NPJ digital medicine Lopez, I., Swaminathan, A., Vedula, K., Narayanan, S., Nateghi Haredasht, F., Ma, S. P., Liang, A. S., Tate, S., Maddali, M., Gallo, R. J., Shah, N. H., Chen, J. H. 2025; 8 (1): 45

    Abstract

    Large language models (LLMs) with retrieval-augmented generation (RAG) have improved information extraction over previous methods, yet their reliance on embeddings often leads to inefficient retrieval. We introduce CLinical Entity Augmented Retrieval (CLEAR), a RAG pipeline that retrieves information using entities. We compared CLEAR to embedding RAG and full-note approaches for extracting 18 variables using six LLMs across 20,000 clinical notes. Average F1 scores were 0.90, 0.86, and 0.79; inference times were 4.95, 17.41, and 20.08 s per note; average model queries were 1.68, 4.94, and 4.18 per note; and average input tokens were 1.1k, 3.8k, and 6.1k per note for CLEAR, embedding RAG, and full-note approaches, respectively. In conclusion, CLEAR utilizes clinical entities for information retrieval and achieves >70% reduction in token usage and inference time with improved performance compared to modern methods.

    View details for DOI 10.1038/s41746-024-01377-1

    View details for PubMedID 39828800

    View details for PubMedCentralID 4287068

  • Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning. Addiction (Abingdon, England) Nateghi Haredasht, F., Fouladvand, S., Tate, S., Chan, M. M., Yeow, J. J., Griffiths, K., Lopez, I., Bertz, J. W., Miner, A. S., Hernandez-Boussard, T., Chen, C. A., Deng, H., Humphreys, K., Lembke, A., Vance, L. A., Chen, J. H. 2024

    Abstract

    Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors.This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data.Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively.Predict continuous prescription of buprenorphine-naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC-AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time-to-event analysis on a single site to estimate the duration of buprenorphine-naloxone treatment continuity evaluated by the C-index and a comparative evaluation against predictions made by three human clinical experts.Attrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC-AUCs up to 75.8 (95% confidence interval [CI] = 73.6-78.0). Addiction medicine specialists' predictions show a ROC-AUC of 67.8 (95% CI = 50.4-85.2). Time-to-event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C-index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence.US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts.

    View details for DOI 10.1111/add.16587

    View details for PubMedID 38923168

  • Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches IEEE ACCESS Venturini, M., Haredasht, F., Sabovcik, F., Miller, R. H., Kuznetsova, T., Vens, C. 2024; 12: 89754-89762

    View details for DOI 10.1109/ACCESS.2024.3418146

    View details for Web of Science ID 001262677900001

https://orcid.org/0000-0002-8874-8835