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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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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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相关实验视频

Updated: Jun 5, 2025

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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通过基于人工智能的多式联络数据融合,高效的临床决策过程:一项COVID-19病例研究.

Daniel I Morís1,2, Joaquim de Moura1,2, Pedro J Marcos3

  • 1Varpa Group, Biomedical Research Institute A Coruña (INIBIC), University of A Coruña, 15006, A Coruña, Spain.

Heliyon
|December 6, 2024
PubMed
概括

一种新的自动化方法使用人工智能通过结合患者数据和X射线图像来预测COVID-19住院和死亡风险. 这种方法可以改善临床决策和资源管理.

关键词:
在 COVID-19 疫情中,胸部X射线 胸部X射线临床数据 临床数据深度功能 功能 功能 功能 深度功能信息融合是一个信息融合.风险估计 风险估计

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相关实验视频

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科学领域:

  • 人工智能在医学中的应用
  • 医学成像分析 医学成像分析
  • 临床决策支持系统 临床决策支持系统

背景情况:

  • 由于COVID-19的流行,需要有效的资源管理和风险分层.
  • 计算机辅助诊断帮助临床医生识别高风险患者.

研究的目的:

  • 开发一种使用多式联络数据融合的COVID-19风险估计完全自动化的方法.
  • 评估COVID-19患者住院和死亡的风险.

主要方法:

  • 利用多式联络数据融合,结合胸部X射线图像中的临床特征和深度特征.
  • 开发了一种新,高效,全自动的机器学习模型.
  • 使用接收器操作特征曲线下的面积 (AUC-ROC) 评估模型性能.

主要成果:

  • 在预测住院 (AUC-ROC 0.8452 ± 0.0133) 和死亡率 (AUC-ROC 0.8285 ± 0.0210) 方面取得了高性能.
  • 确定了每个风险场景的关键特征,显示了临床和成像数据的不同作用.
  • 证明多式联运数据融合优于单源数据方法.

结论:

  • 开发的方法有效地帮助临床决策对COVID-19风险分层.
  • 多式联网数据融合提供了优势,即使具有减少的功能集,有利于资源有限的设置.
  • 该方法显示了在管理其他临床情景方面具有更广泛应用的潜力.