no code implementations • 26 Oct 2018 • Emily Alsentzer, Anne Kim
Patient summarization is essential for clinicians to provide coordinated care and practice effective communication.
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.
no code implementations • 5 Feb 2020 • Matthew B. A. McDermott, Emily Alsentzer, Sam Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019.
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.
no code implementations • 28 Aug 2020 • Irene Y. Chen, Emily Alsentzer, Hyesun Park, Richard Thomas, Babina Gosangi, Rahul Gujrathi, Bharti Khurana
Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue.
no code implementations • 19 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.
no code implementations • NAACL 2021 • Griffin Adams, Emily Alsentzer, Mert Ketenci, Jason Zucker, Noémie Elhadad
Summarization of clinical narratives is a long-standing research problem.
no code implementations • 30 Nov 2021 • Fabian Falck, Yuyin Zhou, Emma Rocheteau, Liyue Shen, Luis Oala, Girmaw Abebe, Subhrajit Roy, Stephen Pfohl, Emily Alsentzer, Matthew B. A. McDermott
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021.
no code implementations • 16 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.
no code implementations • 6 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.