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相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Artificial intelligence (AI) psychosis: mechanisms, clinical risks and safety considerations in generative AI chatbots.

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A Chatbot for the Management of Bipolar Disorder: Using Retrieval-Augmented Generation With an Open-Weight Large Language Model to Answer Clinical Questions Based on the CANMAT and ISBD 2018 Guidelines for Bipolar Disorder: Un dialogueur pour la prise en charge du trouble bipolaire : utiliser la génération augmentée par récupération avec un grand modèle de langage (GML) à poids ouverts pour répondre aux questions cliniques fondées sur les lignes directrices de 2018 de CANMAT et de l'ISBD relatives au trouble bipolaire.

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相关实验视频

Updated: May 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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使用长型大语言模型对非结构化癌症病理报告进行细分

Damien Fung1, Gregory Arbour1, Krisha Malik2

  • 1Department of Computer Science, University of British Columbia, Vancouver, Canada.

JCO clinical cancer informatics
|March 4, 2025
PubMed
概括

这项研究对Longformer模型进行了微调,用于细分癌症病理报告,改善了诊断和临床史等关键部分的隔离,以便更好地进行NLP分析.

科学领域:

  • 自然语言处理 (NLP) 是一种自然语言处理.
  • 计算病理学计算病理学
  • 机器学习用于医疗保健

背景情况:

  • 文本预处理通过删除外部信息来提高NLP模型的性能.
  • 文本细分隔离关键文档部分,有助于下游分析.
  • 像BERT这样的变压器模型在NLP任务中表现出色,但对于冗长的文档有标志性的限制.

研究的目的:

  • 开发和评估用于细分癌症病理学报告的Longformer模型.
  • 解决标准变压器模型在处理长文件时的局限性.
  • 为了提高病理学报告中隔离关键部分的准确性.

主要方法:

  • 一个长期以来的问题-答案 (QA) 模型在504份注释病理学报告上进行了微调.
  • 该模型被训练来识别包括诊断,附录和临床病史在内的部分.
  • 性能与常规表达式和BERT QA等基线方法进行了比较,使用序列回忆,精度和F1评分.

主要成果:

  • 精心调整的Longformer模型在304份测试报告中获得了F1序列的整体得分0.68.
  • 具体的F1分数包括诊断 (0.77),附录 (0.48) 和临床病史 (0.89).
  • 与基线方法相比,Longformer模型在细分病理报告部分方面表现出优异的性能.

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结论:

  • 一个微调的Longformer模型有效地细分了癌症病理学报告.
  • 这种方法提高了隔离关键部分以进行进一步分析的准确性.
  • 开发的模型为计算病理学和NLP应用提供了有价值的工具.