Bio

François Grolleau MD, MPH, PhD is a Postdoctoral Scholar at the Stanford Center for Biomedical Informatics Research. His research work centers on developing and evaluating computational systems that use retrieval-augmented language models and other advanced methods from statistics and machine learning to assist medical decision-making.

François is a certified Anesthesiologist and Critical Care Medicine specialist from France. He holds an MPH degree and a PhD in Biostatistics from Paris Cité University. In 2016/2017, he worked as a research fellow in the Department of Health Research Methods, Evidence, and Impact at McMaster University, Canada (Profs Yannick Le Manach and Gordon Guyatt). During his doctorate with Prof. Raphaël Porcher, he utilized causal inference, personalized medicine methods, and statistical reinforcement learning for medical applications in the ICU.

Professional Education

  • Fellowship, Centre for Research in Epidemiology and Statistics (2023)
  • PhD, Paris Cité University (2023)
  • Board Certification, French Board of Anesthesiology and Critical Care Medicine (2019)
  • Residency, University of Caen Normandy, Critical Care Medicine, Anesthesiology, and Nephrology (2019)
  • MPH, Paris Descartes University (2017)
  • MD, Toulouse III - Paul Sabatier University (2013)

Stanford Advisors

Contact

  • Academic
    grolleau@stanford.edu
    University - Scholar Department: Med/BMIR Position: Postdoctoral Scholar

Additional Info

Links

All Publications

  • Personalizing renal replacement therapy initiation in the intensive care unit: a reinforcement learning-based strategy with external validation on the AKIKI randomized controlled trials JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION Grolleau, F., Petit, F., Gaudry, S., Diard, E., Quenot, J., Dreyfuss, D., Tran, V., Porcher, R. 2024; 31 (5): 1074-1083

    Abstract

    The timely initiation of renal replacement therapy (RRT) for acute kidney injury (AKI) requires sequential decision-making tailored to individuals' evolving characteristics. To learn and validate optimal strategies for RRT initiation, we used reinforcement learning on clinical data from routine care and randomized controlled trials.We used the MIMIC-III database for development and AKIKI trials for validation. Participants were adult ICU patients with severe AKI receiving mechanical ventilation or catecholamine infusion. We used a doubly robust estimator to learn when to start RRT after the occurrence of severe AKI for three days in a row. We developed a "crude strategy" maximizing the population-level hospital-free days at day 60 (HFD60) and a "stringent strategy" recommending RRT when there is significant evidence of benefit for an individual. For validation, we evaluated the causal effects of implementing our learned strategies versus following current best practices on HFD60.We included 3748 patients in the development set and 1068 in the validation set. Through external validation, the crude and stringent strategies yielded an average difference of 13.7 [95% CI -5.3 to 35.7] and 14.9 [95% CI -3.2 to 39.2] HFD60, respectively, compared to current best practices. The stringent strategy led to initiating RRT within 3 days in 14% of patients versus 38% under best practices.Implementing our strategies could improve the average number of days that ICU patients spend alive and outside the hospital while sparing RRT for many.We developed and validated a practical and interpretable dynamic decision support system for RRT initiation in the ICU.

    View details for DOI 10.1093/jamia/ocae004

    View details for Web of Science ID 001180151600001

    View details for PubMedID 38452293

    View details for PubMedCentralID PMC11031229

https://orcid.org/0000-0001-7873-7772