Emily Alsentzer

Emily Alsentzer

PhD Student

MIT & Harvard Medical School

Biography

I’m a PhD student in Health Science & Technology (HST) at MIT & Harvard Medical School where I’m co-advised by Zak Kohane and Pete Szolovits. I am working to develop machine learning tools that can enable clinicians by helping them quickly find and interpret abundant patient data. I am currently focusing on developing few-shot methods for the diagnosis of patients with rare genetic diseases in the Undiagnosed Disease Network, but am more broadly interested in research that addresses barriers to clinical deployment (e.g. learning with limited data, human-in-the-loop data annotation and deployment, etc.).

Interests
  • Deployable ML
  • Human-in-the-loop ML
  • Few Shot Learning
  • NLP & Graph Neural Networks
  • Summarization
  • Rare Disease Diagnosis
Education
  • PhD in Medical Engineering & Medical Physics (HST), 2022 (expected)

    Massachusetts Institute of Technology

  • MS in Biomedical Informatics, 2017

    Stanford University

  • BS in Computer Science, 2016

    Stanford University

Recent Publications

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(2021). What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization. arXiv preprint arXiv:2105.00816.

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(2020). Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting. Emerging infectious diseases.

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(2020). Baselines for chest x-ray report generation. Machine Learning for Health Workshop.

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(2020). Intimate partner violence and injury prediction from radiology reports. BIOCOMPUTING 2021: Proceedings of the Pacific Symposium.

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(2020). Machine Learning for Health (ML4H) 2019: What Makes Machine Learning in Medicine Different?. Machine Learning for Health Workshop.

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Contact

  • 32 Vassar Street, Cambridge, Massachusetts 02139
  • DM Me