Search Results for author: Emily Alsentzer

Found 8 papers, 2 papers with code

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.

De-identification

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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