Search Results for author: Emily Alsentzer

Found 10 papers, 2 papers with code

Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models

no code implementations6 Feb 2024 Brenda Y. Miao, Christopher YK Williams, Ebenezer Chinedu-Eneh, Travis Zack, Emily Alsentzer, Atul J. Butte, Irene Y. Chen

Notably, we also showed using our approach that "weight gain/mood change" and "insurance coverage" are disproportionately found as reasons for contraceptive switching in specific demographic populations.

Language Modelling Large Language Model

Do We Still Need Clinical Language Models?

no code implementations16 Feb 2023 Eric Lehman, Evan Hernandez, Diwakar Mahajan, Jonas Wulff, Micah J. Smith, Zachary Ziegler, Daniel Nadler, Peter Szolovits, Alistair Johnson, Emily Alsentzer

To investigate this question, we conduct an extensive empirical analysis of 12 language models, ranging from 220M to 175B parameters, measuring their performance on 3 different clinical tasks that test their ability to parse and reason over electronic health records.

In-Context Learning

ML4H Abstract Track 2020

no code implementations19 Nov 2020 Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck, Suproteem K. Sarkar, Subhrajit Roy, Stephanie L. Hyland

A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020.

BIG-bench Machine Learning

Subgraph Neural Networks

1 code implementation NeurIPS 2020 Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, Marinka Zitnik

Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks.

Publicly Available Clinical BERT Embeddings

2 code implementations WS 2019 Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, Matthew B. A. McDermott

Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP) tasks in recent months.


Extractive Summarization of EHR Discharge Notes

no code implementations26 Oct 2018 Emily Alsentzer, Anne Kim

Patient summarization is essential for clinicians to provide coordinated care and practice effective communication.

Decision Making Extractive Summarization

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