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Techniques of Therapeutic Communication II: Focusing, Paraphrasing, and Summarizing01:23

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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Nursing documentation encompasses various formats designed to capture precise patient data, facilitate communication among healthcare team members, and ensure comprehensive and accurate patient records. Let's explore each of these formats in detail:
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临床总结:利用语言模型从患者与医生的对话中生成临床总结.

Subash Neupane1, Himanshu Tripathi1, Shaswata Mitra1

  • 1Dept. of Computer Science and Engineering, Mississippi State University Potentia Analytics Inc.; Dave C. Swalm School of Chemical Engineering, Mississippi State University.

Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data
|September 8, 2025
PubMed
概括
此摘要是机器生成的。

ClinicSum自动生成来自患者与医生的对话的临床摘要,使用两个模块框架. 这种新的方法,利用检索和预训练语言模型 (PLM),在准确性和专家评估方面超过现有方法.

关键词:
临床总结 临床总结精细调整 微调 精细调整在 PLM PLM 中.在RAG RAG的基础上.肥 肥 是一种肥.总结 总结 总结 总结

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科学领域:

  • 医疗信息学 医疗信息学
  • 自然语言处理自然语言处理.
  • 人工智能的人工智能

背景情况:

  • 临床总结患者与医生的对话对于高效的医疗保健文档至关重要.
  • 现有的方法在捕获必要的临床信息时,往往在准确性和完整性方面扎.
  • 自动化这个过程可以减轻临床医生的负担,提高数据质量.

研究的目的:

  • 推出ClinicSum,这是一个用于自动生成临床摘要的新框架.
  • 利用一个双模块架构,将检索和预训练语言模型 (PLM) 结合起来,以实现增强的总结.
  • 通过使用自动化指标和专家评估,与最先进的方法对比评估ClinicSum的表现.

主要方法:

  • 开发了一个基于检索的模块来提取主观,目标,评估和计划 (SOAP) 信息.
  • 在一个推理模块中使用微调的PLM来从SOAP数据中生成抽象的临床摘要.
  • 从公共数据集 (FigShare,MTS-Dialog) 创建了一个由1473个对话-摘要对组成的培训数据集,并通过了主题专家 (SME) 的验证.

主要成果:

  • 与最先进的PLM相比,ClinicSum表现优越.
  • 在自动评估 (ROUGE,BERTScore) 中获得了更高的精度,回忆和F-1分数.
  • 在人体评估中获得了中小企业的高优先级评级,表明临床实用性.

结论:

  • 临床总结为自动化临床总结提供了强大而有效的解决方案.
  • 该框架显示了提高临床文档效率和准确性的巨大潜力.
  • 进一步开发可以将ClinicSum集成到临床工作流程中,以支持医疗保健专业人员.