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This project leverages Python programming and an open-source large language model (LLM) to calculate semantic similarity scores between pairs of text strings. These scores are systematically recorded in an Excel workbook, enabling the organization of string pairings into ranked mappings. Dr. Macintosh employed this methodology to propose mappings between AACN graduate sub-competencies and course learning outcomes.

This presentation is an introduction to Oncology Data Science at AstraZeneca and the important work being done to impact decision making to advance AstraZeneca’s oncology drug portfolio. Steven Criscione will also share a research vignette on our study of the EGFR inhibitor osimertinib drug tolerant persister cells.

Large language models (LLMs) hold immense promise for democratizing access to medical information and assisting physicians in delivering higher-quality care. However, realistic evaluations of LLMs in clinical contexts have been limited, with much focus placed on multiple-choice evaluations of clinical knowledge. In this talk, I will present a four-level framework [...]

With growing legal and scientific evidence for the importance of reducing model bias, both model developers and deployers need tools to quantify the bias. Unfortunately, algorithmic bias can take as many forms as there are implementations. In this talk, Paul M. Heider covesr a range clinical NLP use cases like [...]