Marc Ghanem

Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine

Bio

Marc graduated with a Medical Degree from the Lebanese American University (LAU), where he also completed his undergraduate studies in Biology with a focus on bioinformatics research. Throughout his medical education, his research focused on applying machine learning to clinical medicine. Marc's collaborative research efforts included work with institutions such as Mayo Clinic and Thomas Jefferson Hospital.

Currently, Marc is a postdoctoral scholar at the Aghaeepour Lab. His research interests center on using deep learning models to identify clinically relevant patterns in large medical datasets to provide personalized and predictive healthcare. His specific areas of interest include: building an intraoperative foundation model, training deep learning models for subgroup discovery, and utilizing models’ patient embeddings for downstream predictions.

Honors & Awards

Best Capstone Project, Lebanese American University (05/2019)
Mayo Clinic Neuro-Informatics Research Collaboration Award, Mayo Clinic Neuro-Informatics Lab (07/2021)
Excellence in Innovation Award, Lebanese American University (05/2023)
Outstanding Researcher Award, Lebanese American University (05/2023)

Current Research and Scholarly Interests

Currently, at the Aghaeepour Lab, his research involves:

Using topic modeling techniques to analyze and interpret trends in anesthesiology research literature.[Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends. Heliyon]

Building predictive machine learning models to optimize total parenteral nutrition (TPN) for neonates, aiming to improve clinical outcomes. [AI-driven Precision Total Parenteral Nutrition in Neonatal Intensive Care Units with TPN2.0. Nature Medicine]

Building a comprehensive preoperative, intraoperative, and postoperative foundation model. This model identifies patient-specific patterns to provide personalized anesthesia recommendations and continuously monitors patient states during surgery. By analyzing intraoperative data, the model can detect previously undiagnosed conditions and assess postoperative complication risks, transforming routine surgical procedures into opportunities for early diagnosis and improved patient outcomes.

Identifying differential responders and their characteristics in clinical trials.

All Publications

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