Emily Alsentzer

Emily Alsentzer

Postdoctoral Fellow

Brigham and Women's Hospital & Harvard Medical School

I’m on the job market this fall 2023! If you know of any positions that would be a good fit, please let me know at ealsentzer at bwh.harvard.edu.

My research addresses the challenges of applying machine learning and natural language processing to healthcare. I work to develop tools that can augment clinicians and patients by helping them quickly find and interpret abundant medical data. My work is motivated by two core questions: (1) How do we develop generalizable models that can learn with limited annotated data? and (2) How can we design trustworthy human-in-the-loop tools that augment clinician decision making?

I am currently a postdoctoral fellow at Brigham and Women’s Hospital and Harvard Medical School (HMS) where I am working to deploy ML models within the Mass General Brigham healthcare system. I completed my PhD in the Health Science & Technology (HST) program at MIT & HMS, co-advised by Zak Kohane and Pete Szolovits. During my PhD, I created ClinicalBERT, a language model trained on electronic health records that has millions of downloads on HuggingFace, and developed SHEPHERD, a graph neural network approach for the diagnosis of patients with rare genetic diseases in the Undiagnosed Disease Network.

  • Deployable Machine Learning
  • Few Shot Learning
  • Natural Language Processing & LLMs
  • Graph Neural Networks
  • Summarization
  • Rare Disease Diagnosis
  • PhD in Medical Engineering & Medical Physics (HST), 2022

    Massachusetts Institute of Technology

  • MS in Biomedical Informatics, 2017

    Stanford University

  • BS in Computer Science, 2016

    Stanford University

[July 2023] Our work on assessing racial and gender bias in GPT-4 for medical applications was featured in Stat News. Our work on few shot diagnosis of rare disease patients received a Best Oral Presentation Award at ISMB.

[June 2023] Our paper Do we still need clinical language models? received a Best Paper Award at CHIL 2023.

[April 2023] I was awarded a grant from Microsoft’s Accelerate Foundation Models Research Initiative to study the use of LLMs for clinical summarization.

[August 2022] I started a postdoctoral fellowship at Brigham and Women’s Hospital and Harvard Medical School with David Bates.

[June 2022] I successfully defended my thesis on Few Shot Learning for Rare Disease Diagnosis. My thesis committee included Zak Kohane, Pete Szolovits, and Marinka Zitnik.

Recent Publications

All publications»

Coding Inequity: Assessing GPT-4's Potential for Perpetuating Racial and Gender Biases in Healthcare. medRxiv, 2023.
Do We Still Need Clinical Language Models?. Conference on Health, Inference, and Learning, 2023.
Towards Medical Billing Automation: NLP for Outpatient Clinician Note Classification. medRxiv, 2023.
Zero-shot Interpretable Phenotyping of Postpartum Hemorrhage Using Large Language Models. medRxiv, 2023.
Deep learning for diagnosing patients with rare genetic diseases. medRxiv, 2022.