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相关概念视频

Issues And Trends In Healthcare Delivery System01:29

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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.
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The placebo effect occurs when people's expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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使用人工智能技术预测患者对药物的看法.

Amir Sorayaie Azar1,2, Samin Babaei Rikan2, Amin Naemi1

  • 1SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.

Scientific reports
|December 31, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了一个人工智能 (AI) 模型用于药物情绪分析. 深层组合模型在预测患者情绪方面取得了高准确性,有助于临床决策.

关键词:
深度学习是一种深度学习.组合学习学习 组合学习可解释的人工智能机器学习 机器学习药物评价 药物评价 药物评价患者情绪分析分析

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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
  • 计算语言学 计算语言学

背景情况:

  • 数字健康数据的扩散需要先进的方法来分析患者对药物的反.
  • 医学领域的情感分析 (SA) 提供了对患者体验和治疗有效性的宝贵见解.

研究的目的:

  • 开发和评估人工智能 (AI) 模型,从药物评价中预测患者情绪.
  • 探索不同的词嵌入技术和分类场景对情绪预测准确性的影响.

主要方法:

  • 利用大规模的药物审查数据集进行两,三和十类场景的情绪分析.
  • 开发了七个机器学习 (ML) 和深度学习 (DL) 模型,包括Word2Vec和预训练嵌入 (一般和临床领域).
  • 实施集体学习以创建深层集体模型 (DL_ENS),并引入了一种可解释性技术.

主要成果:

  • 使用PubMed和PMC嵌入式的深层组合模型 (DL_ENS) 实现了卓越的性能,在两类场景中获得了最高准确率 (92.96%) 和F1-Score (92.27%).
  • DL_ENS模型在所有分类场景 (二,三和十类) 中表现出强大的预测能力.
  • 开发的模型在决策过程中提供了透明度,提高了其对临床应用的有用性.

结论:

  • 将DL模型与临床领域嵌入组合到DL_ENS中,可提供准确的药物患者情绪预测.
  • DL_ENS模型是临床医生的一种有价值的辅助工具,支持知情的药物处方.
  • 这项研究推动了人工智能的应用,用于分析患者反以改善医疗保健结果.