5 papers
Fine-Tune, Don't Prompt, Your Language Model to Identify Biased Language in Clinical Notes
Isotta Landi, Eugenia Alleva, Nicole Bussola +5
Clinical documentation can contain emotionally charged language with stigmatizing or privileging valences. We present a framework for detecting and classifying such language as sti…
JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures
Ariel Larey, Elay Dahan, Amit Bleiweiss +15
Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature". While effective at capturing local s…
Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities
Alissa A. Valentine, Lauren A. Lepow, Donald Apakama +3
The emergency department (ED) is a high stress environment with increased risk of clinician bias exposure. In the United States, Black patients are more likely than other racial/et…
Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study
Alissa A. Valentine, Alexander W. Charney, Isotta Landi
As interest in implementing artificial intelligence (AI) in medical systems grows, discussion continues on how to evaluate the fairness of these systems, or the disparities they ma…
The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes
Alissa A. Valentine, Lauren A. Lepow, Lili Chan +2
Negative patient descriptions and stigmatizing language can contribute to generating healthcare disparities in two ways: (1) read by patients, they can harm their trust and engagem…