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Pulmonary embolism (PE) presents a significant diagnostic challenge, necessitating clear and precise communication between radiologists and referring physicians to optimize patient outcomes. Variability in the language used to convey diagnostic uncertainty in computed tomographic pulmonary angiography (CTPA) reports can exacerbate ambiguity, potentially leading to inappropriate clinical management and compromised patient care.

This webinar provides an opportunity to hear from the editors at Nature portfolio journal (npj) Digital Medicine and Health Systems to understand their journals’ scopes specifically with respect to NLP research. Audience will have an opportunity to ask questions. Presenters Watch the Recording

This session will introduce you to the various opportunities for involvement in public policy through AMIA/NIWG. Our goal is to help those interested in policy understand the landscape of opportunities available and emphasize the crucial role your voices play in shaping public policy. The webinar will cover why nurses’ involvement is vital and how you can participate.

The Women in AMIA Career Advancement Subcommittee would like to invite you to an upcoming webinar, “Empower Your Worth: Mastering Salary Negotiation for Women.” Please join three senior female informatics leaders as they discuss best practices and techniques for salary negotiation, for internal promotions and for securing new opportunities.

Clinician burnout, driven in part by the increasing burden of EHR documentation, is a significant threat to healthcare quality. This talk will introduce SPEER, a novel approach to automatically generating clinically useful hospital-course summaries from raw clinical notes.

Human language is complex and often equivocal. Unsurprisingly, even the most sophisticated natural language processing (NLP) algorithms inevitably make mistakes. The impact of these mistakes on the results of clinical research that uses NLP-generated data as one of the inputs is uncertain. In this talk, Alexander Turchin will discuss a [...]

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.