Resources

Clinical risk prediction based on machine learning algorithms plays a vital role in modern healthcare. A crucial component in developing a reliable prediction model is a high-quality dataset with time series clinical events. In this work, we release…

Electronic Health Records (EHRs) combine structured data, such as medications and laboratory measurements, with unstructured clinical notes describing patient conditions and clinical events. Although these two data types provide complementary inform…

Transformer-based neural language models have led to breakthroughs for a variety of natural language processing (NLP) tasks. However, most models are pretrained on general domain data. We propose a methodology to produce a model focused on the clini…