认知计算在加强创新医疗解决方案中的作用

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引用次数: 0

摘要

认知计算代表着医疗保健领域的突破性发展,它包括模拟人脑功能的技术平台。云计算提供按需访问互联网的计算资源和服务,而认知计算则侧重于模拟人类的心理过程,以解决复杂的问题。认知计算通过整合推理、机器学习、语音、自然语言处理(NLP)和人机交互来增强人类的决策能力。在医疗保健领域,认知计算有助于分析临床和基因数据,以预测疾病、定制治疗方案并促进药物开发。此外,它还能将数据分析与自适应页面显示相结合,根据受众情况定制内容。本文确定并研究了认知计算在医疗保健领域的相关论文。本文旨在对各种来源的相关文献(包括众多期刊和会议论文集中的文章和文件)进行广泛的综述。它深入探讨了医疗保健领域对认知计算的需求,阐明了支持性技术,并阐述了其在医疗保健领域的特点。此外,它还确定并讨论了认知计算在医疗保健领域的大量应用。这些系统利用计算机模型复制人类的认知过程,通过人工智能和认知计算简化管理任务。因此,医疗保健管理人员可以将更多宝贵时间分配给病人护理工作。认知计算可提高疗效和从业人员的工作效率,并改进治疗决策。认知计算的自学习系统依赖于实时患者数据、医疗记录和其他相关信息。这些技术可以自动执行发票开具、索赔处理和预约安排等任务,从而减轻医护人员的行政负担。在精准医疗领域,这项技术将变得越来越不可或缺。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Role of cognitive computing in enhancing innovative healthcare solutions

Cognitive computing represents a groundbreaking development in healthcare, encompassing technological platforms that emulate the human brain's functionality. While cloud computing offers on-demand internet access to computing resources and services, cognitive computing focuses on modelling human mental processes to tackle complicated issues. Cognitive computing enhances human decision-making by integrating reasoning, machine learning, speech, natural language processing (NLP), and human-computer interaction. In the healthcare sector, it facilitates the analysis of clinical and genetic data to forecast diseases, tailor therapies, and elevate drug development. Additionally, it combines data analysis with adaptive page displays to tailor content based on the audience. Relevant papers in cognitive computing for healthcare were identified and studied. This paper aims to undertake an extensive scopic review of the pertinent literature from various sources, including articles and documents from numerous journals and conference proceedings. It delves into the need for cognitive computing in healthcare, elucidates supportive technologies, and expounds on its features within the healthcare domain. Furthermore, it identifies and discusses the substantial applications of cognitive computing in healthcare. These systems utilise computer models to replicate human cognitive processes, streamlining administrative tasks through artificial intelligence and cognitive computing. As a result, healthcare administrators can allocate more of their valuable time to patient care. Cognitive computing enhances outcomes and practitioner productivity and improves treatment decisions. The self-learning system of cognitive computing relies on real-time patient data, medical transcripts, and other pertinent information. These technologies can reduce the administrative burden on healthcare workers by automating tasks such as invoicing, claims processing, and appointment scheduling. This technology is poised to become increasingly indispensable in precision medicine.

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Advances in biomarker sciences and technology
Advances in biomarker sciences and technology Biotechnology, Clinical Biochemistry, Molecular Medicine, Public Health and Health Policy
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