732 citations · 740 across the 2 of their papers we have counts for
9 papers
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
Bret Nestor, Matthew B. A. McDermott, Willie Boag +5
When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as ca…
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)…
Clinically Accurate Chest X-Ray Report Generation
Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew McDermott +4
The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care. A number of prior works have f…
Unsupervised Multimodal Representation Learning across Medical Images and Reports
Tzu-Ming Harry Hsu, Wei-Hung Weng, Willie Boag +2
Joint embeddings between medical imaging modalities and associated radiology reports have the potential to offer significant benefits to the clinical community, ranging from cross-…
Racial Disparities and Mistrust in End-of-Life Care
Willie Boag, Harini Suresh, Leo Anthony Celi +2
There are established racial disparities in healthcare, including during end-of-life care, when poor communication and trust can lead to suboptimal outcomes for patients and their…
Modeling Mistrust in End-of-Life Care
Willie Boag, Harini Suresh, Leo Anthony Celi +2
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score. We show that this score has statistically significant racial association…