article-journal

We explore how unstructured clinical text can be used to reduce selection bias and improve medical practice. We present this proof-of-concept study to enable more credible causal inference using observational data, uncover meaningful insights from clinical text, and inform high-stakes medical decisions.

We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer.

We developed a probabilistic model to support the classification decisions made by radiologists in mammography practice. Using the …

We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making.