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- W4368359970 abstract "Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.心力衰竭是一种严重危害人类健康的疾病，已成为全球公共卫生问题。基于医学影像、临床等数据进行心力衰竭诊断与预后分析能揭示心力衰竭的病程规律，从而降低患者死亡风险，具有重要研究价值。传统基于统计学与机器学习的分析方法存在模型能力不足、先验依赖造成的准确性差、模型适应性不佳等问题。近年来，随着人工智能技术的发展，深度学习方法逐渐开始在心力衰竭领域的临床数据分析应用中展现出新的前景。本文综述深度学习在心力衰竭诊断、心力衰竭生存风险、心力衰竭再入院等方面的主要工作进展、应用方式与主要成果，总结目前存在的问题，提出相关研究展望，以促进深度学习在心力衰竭临床研究的临床应用。." @default.
- W4368359970 created "2023-05-05" @default.
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- W4368359970 date "2023-04-25" @default.
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- W4368359970 title "[Advances in heart failure clinical research based on deep learning]." @default.
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- W4368359970 doi "https://doi.org/10.7507/1001-5515.202208060" @default.
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