Biomedical Informatics Commons^™

Detecting Stigmatizing Language In Clinical Notes With Large Language Models For Addiction Care, Rohan Sethi, John Caskey, Yanjun Gao, Matthew M. Churpek, Timothy A. Miller, Anoop Mayampurath, Elizabeth Salisbury-Afshar, Majid Afshar, Dmitriy Dligach

Computer Science: Faculty Publications and Other Works

Intensive care units (ICU) produce numerous progress notes that may contain stigmatizing language that perpetuate negative biases and punitive approaches against patients. Patients with substance use disorders are particularly vulnerable to stigma. This study examined the performance of Large Language Models (LLMs) in the identification of stigmatizing language. We annotated a dataset with over 77,000 stigmatizing and non-stigmatizing notes from the MIMIC-III database. We utilized Meta's Llama-3 8B Instruct LLM to run the following experiments for stigma detection: zero-shot; in-context learning; in-context learning with a selective retrieval; supervised fine-tuning (SFT); and keyword search. All approaches were evaluated on a held-out …

Sub-Saharan Africa’S Handling Of Covid-19 Pandemic: Rwanda Versus Tanzania, Luke D. Baumel

Honors Undergraduate Conference

How has the Covid-19 outbreak in early 2020 and the pandemic that followed been handled in Sub-Saharan Africa? What was done well and what was done poorly and why? What are the implications of the way it was handled? These questions and more are answered throughout this paper, using Rwanda as an example of a Sub-Saharan African country who handled it relatively well and neighboring Tanzania as an example of a Sub-Saharan African country that mishandled the outbreak and pandemic. In early 2021, most Sub-Saharan African countries had been afflicted by Covid-19 at a relatively lower rate than many Western …

Deployment Of Real-Time Natural Language Processing And Deep Learning Clinical Decision Support In The Electronic Health Record: Pipeline Implementation For An Opioid Misuse Screener In Hospitalized Adults, Majid Afshar, Sabrina Adelaine, Felice Resnik, Marlon P. Mundt, John Long, Margaret Leaf, Theodore Ampian, Graham J. Wills, Benjamin Schnapp, Michael Chao, Randy Brown, Cara Joyce, Brihat Sharma, Dmitry Dligach, Elizabeth S. Burnside, Jane Mahoney, Matthew M. Churpek, Brian W. Patterson, Frank Liao

Biostatistics Collaborative Core: Faculty Publications and Other Works

Background: The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery.

Objective: We aimed to detail a hospital-wide, operational pipeline to implement a real-time NLP-driven CDS tool and describe a protocol for an implementation framework with a user-centered design of the CDS tool.

Methods: The pipeline integrated …