Article

Entity-Aware Generation of Synthetic Clinical Progress Notes for Prostate Cancer using Large Language Models

2026-06-15

Abstract excerpt

<h4>Objectives</h4> This study investigates large language models (LLMs) for clinical entity projection across substantial textual transformation. Specifically, we evaluate whether entities annotated in Spanish prostate cancer case reports can be preserved and explicitly projected when the source narratives are transformed into hospital-style clinical progress notes. Entity projection is treated as a generation-d...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: b0ac259b-6275-54d1-a5a3-6394047e4be2
DOI: 10.64898/2026.06.12.26355166

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Entity-Aware Generation of Synthetic Clinical Progress Notes for Prostate Cancer using Large Language ModelsDOI 10.64898/2026.06.12.26355166Read

Generation and Evaluation of Realistic Synthetic Clinical Progress Notes for Prostate Cancer using Large Language ModelsSemantic proximity Read

Exploration of Genetic Entity Extraction From Spanish Literature Using Generative LLMs.Semantic proximity Read

The Aluminum Standard: Using Generative Artificial Intelligence Tools to Synthesize and Annotate Non-Structured Patient DataSemantic proximity Read

A Framework for Extraction of Clinical Information from Radiological Mammography Reports Using Large Language Models and Retrieval Augmented Generation.Semantic proximity Read

mCodeGPT: Enhancing Cancer Research through Zero-Shot Information Extraction from Clinical Free Text DataSemantic proximity Read

Comparative analysis of discriminative and generative natural language processing pipelines for automated prostate magnetic resonance imaging reportsSemantic proximity Read

Synoptic Reporting by Summarizing Cancer Pathology Reports using Large Language ModelsSemantic proximity Read

Phenotyping Prostate Cancer in a National Health System Using Large Language Models.Semantic proximity Read

Iterative Feedback--Refinement for Faithful Structured Clinical-Note RepresentationSemantic proximity Read

Combining Clinician Expertise with Prompt Engineering enhances Small Language Models Reliability for Cancer Entity Recognition in Electronic Health RecordsSemantic proximity Read

C-RLM: Schema-Enforced Recursive Synthesis for Auditable, Long-Context Clinical DocumentationSemantic proximity Read

RaTEScore: A Metric for Radiology Report GenerationSemantic proximity Read