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Found 9 papers, 6 papers with code
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
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 care practices, database systems, and population demographics evolve.
Publicly Available Clinical BERT Embeddings
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.
Clinically Accurate Chest X-Ray Report Generation
The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care.
Unsupervised Multimodal Representation Learning across Medical Images and Reports
Joint embeddings between medical imaging modalities and associated radiology reports have the potential to offer significant benefits to the clinical community, ranging from cross-domain retrieval to conditional generation of reports to the broader goals of multimodal representation learning.
Racial Disparities and Mistrust in End-of-Life Care
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 families.
Applications
Modeling Mistrust in End-of-Life Care
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score.
Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes
Clinical notes often describe the most important aspects of a patient's physiology and are therefore critical to medical research.
CliNER 2.0: Accessible and Accurate Clinical Concept Extraction
Clinical notes often describe important aspects of a patient's stay and are therefore critical to medical research.
AWE-CM Vectors: Augmenting Word Embeddings with a Clinical Metathesaurus
In recent years, word embeddings have been surprisingly effective at capturing intuitive characteristics of the words they represent.
