732 citations · 821 across the 7 of their papers we have counts for
7 papers
Parkinsonian Chinese Speech Analysis towards Automatic Classification of Parkinson's Disease
Hao Fang, Chen Gong, Chen Zhang +2
Speech disorders often occur at the early stage of Parkinson's disease (PD). The speech impairments could be indicators of the disorder for early diagnosis, while motor symptoms ar…
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
Griffin Adams, Emily Alsentzer, Mert Ketenci +2
Summarization of clinical narratives is a long-standing research problem. Here, we introduce the task of hospital-course summarization. Given the documentation authored throughout…
Intimate Partner Violence and Injury Prediction From Radiology Reports
Irene Y. Chen, Emily Alsentzer, Hyesun Park +4
Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue. We present machine learning models to assess patients for IPV and injury. We train…
Subgraph Neural Networks
Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li +1
Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks. However, despite the proliferation of the methods and their suc…
ML4H Abstract Track 2019
Matthew B. A. McDermott, Emily Alsentzer, Sam Finlayson +5
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019. This index is not complete, as some accepted abstracts chose to opt-out…
Publicly Available Clinical BERT Embeddings
Emily Alsentzer, John R. Murphy, Willie Boag +4
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)…
Extractive Summarization of EHR Discharge Notes
Emily Alsentzer, Anne Kim
Patient summarization is essential for clinicians to provide coordinated care and practice effective communication. Automated summarization has the potential to save time, standard…