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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.
Cost Containment
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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
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相关实验视频

Updated: Jul 24, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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基于人工智能的语音分析系统用于医疗支持.

Eui-Sun Kim1, Dong Jin Shin2, Sung Tae Cho3

  • 1Department of Media, Soongsil University, Seoul, Korea.

International neurourology journal
|July 4, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种人工智能驱动的语音分析系统,用于在中风患者中早期检测神经性膀. 该系统在识别语音异常方面达到很高的准确性,有助于及时干预和预防.

关键词:
深度学习是一种深度学习.诊断支持系统的诊断支持系统神经性膀发生器.语音识别 语音识别 语音识别一次性中风,中风.

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科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 语音分析 语言分析

背景情况:

  • 脑卒中可以显著影响神经性膀症状和表现.
  • 在患有神经性膀的中风患者中观察到异常的语言和面部特征.
  • 早期检测和预防对于管理与中风相关的神经性膀至关重要.

研究的目的:

  • 开发基于人工智能的语音分析平台,用于神经性膀中风患者的准确语音分析.
  • 为了能够在中风幸存者中早期检测和预防神经性膀后果.

主要方法:

  • 开发了一个基于人工智能的语音分析诊断系统.
  • 分析了发出特定句子的中风患者的语音记录,以寻找独特的特征数据.
  • 一个移动应用程序被用来提供语音报警服务,分类异常并发出警报.

主要成果:

  • 人工智能系统实现了高测试准确性:正常数据为98.7%,异常数据为99.6%.
  • 该模型使用训练和测试数据进行验证,实时处理30个异常和30个正常数据点.
  • 该系统在对中风患者与神经性膀相关的语音异常进行分类时表现出了强大的性能.

结论:

  • 人工智能驱动的数字治疗对于管理诸如中风等导致长期严重损伤的慢性疾病至关重要.
  • 开发的AI医疗保健设备为中风患者提供及时和安全的移动医疗服务.
  • 这种方法旨在通过改善患者护理和预防中风相关的神经性膀严重后果来降低国家社会成本.