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E. Oermann

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#natural language process... Preprint Oct 2026

Incidental information contaminates patient notes and disrupts clinical reasoning in large language models

Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failure mode shared between these two applications by assessing their sensitivity to information incidental to the patient encounter. In 576 patient-clinician dialogues, we...

K. Vishwanath, Brandon Ye, A. Alyakin et al. · 0 citations
#machine learning Preprint Sep 2026

Reason in Style: Discovering and Controlling Style in Language Models

Language models learn content and style jointly, making stylistic variation in their outputs difficult to identify and control. We study whether recurring styles in model responses can be discovered without supervision and explicitly controlled. We design an algorithm that learns to separate representations of content...

Ioana Marinescu, E. Oermann, Kyunghyun Cho · 0 citations
Open access Sep 2026

An interpretable peptide–HLA model emergently learns binding energetics and structure

The range of peptides a human leukocyte antigen (HLA) binds and displays modulates immune response and therefore underpins vaccine design, neoantigen discovery, autoimmunity, transplantation, and hypersensitivity reactions. Modern predictors of peptide–HLA (pMHC) binding and presentation are remarkably accurate, but th...

Sully F. Chen, Robert J. Steele, E. Oermann · 0 citations
Open access Jul 2026

Advancing cancer detection and treatment using longitudinal routine clinical data.

Oncoformer provides a framework for risk-informed cancer prediction and treatment stratification using routine clinical data and was independently validated against postoperative pathological endpoints and shown to converge on core cancer genomic pathways.

Fei Liu, Kai Wang, Hui Xu et al. · 2 citations
#software testing Open access Sep 2026

Spinal meningiomas: histopathological grading using a benchmark radiomics model with notes on disease control.

A benchmark radiomics model to preoperatively identify the histological grade of spinal meningiomas is constructed, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs.

Adhith Palla, Nicolas K. Goff, Blake Perdikis et al. · 0 citations
Review Aug 2026

Big data in U.S. neuro-oncology: trends and translational priorities

An overview of the landscape of major U.S. neuro-oncology data resources is provided and how these datasets are used in contemporary research is evaluated, including population registries, clinical data networks, federal and consortium research cohorts, institutional datasets, specialized resources, and artificial inte...

Anjali Kapoor, A. Alyakin, J. Markert et al. · 0 citations

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