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.
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.
The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care.
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.
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.
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score.
Clinical notes often describe the most important aspects of a patient's physiology and are therefore critical to medical research.
Clinical notes often describe important aspects of a patient's stay and are therefore critical to medical research.