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相关概念视频

Health Literacy01:21

Health Literacy

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Health literacy is an individual's or a community's capacity to comprehend, receive, read, and use relevant healthcare information and services. The World Health Organization (WHO, 2018) defines health literacy as the cognitive and social skills that determine the ability of individuals to gain access to, understand, and use information in ways that promote and maintain good health. As a result, the WHO helps individuals manage long-term health concerns, participate in preventative...
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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
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相关实验视频

Updated: Jun 29, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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医疗保健应用的高效机器阅读理解:算法开发和文本提取方法的验证.

Duy-Anh Nguyen1, Minyi Li2, Gavin Lambert3,4

  • 1School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, Australia.

JMIR formative research
|March 25, 2024
PubMed
概括

本研究引入了一种新的上下文提取方法,以改进机器阅读理解 (MRC) 模型. 新方法提高了准确性,并大大减少了复杂,长文本域的处理时间.

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背景提取 背景提取 背景提取在COVID19中,有很多人受到影响.医疗保健 医疗保健 医疗保健机器阅读理解 阅读理解问题 回答 回答 问题 回答

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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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相关实验视频

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科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 提取式机器阅读理解 (MRC) 模型在开放领域表现出色,但在医疗保健等复杂,广泛的上下文领域扎.
  • 较长的背景导致MRC模型的准确性降低和预测速度减慢.
  • 通过提取仅相关信息来减少输入上下文是一个潜在的解决方案.

研究的目的:

  • 为MRC任务开发一种有效的上下文提取方法.
  • 为了使MRC模型能够更高效,更准确地处理长篇文章.
  • 提高专业领域的问答能力.

主要方法:

  • 开发了一种新的方法来估计在给定的背景下回答问题的句子实用性.
  • 训练了两种模型,根据经验研究和MRC模型信心评分来预测句子的实用性.
  • 根据句子实用性估计,为MRC模型提取了一个更短,更精确的上下文.

主要成果:

  • 在COVID-19和生物医学QA数据集上表现出有效性.
  • 推断时间缩短了6~7倍.
  • 改善了MRC模型的准确性,F1分数从0.724增加到0.744 (COVID-19) 和0.651增加到0.704 (生物医学).

结论:

  • 拟议的上下文提取方法提高了MRC预测的准确性,并大大减少了推断时间.
  • 该技术与任何MRC模型兼容,适用于涉及广泛文本处理的任务.
  • 在采掘变压器MRC模型即使在精确的背景下也可能仍然表现不佳的情况下,存在潜在的挑战.