利用数字时代的计算工具加强感染控制。
Harnessing computational tools of the digital era for enhanced infection control.
机构信息
Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico di Roma, Rome, Italy.
出版信息
BMC Med Inform Decis Mak. 2024 Sep 12;24(1):252. doi: 10.1186/s12911-024-02650-9.
Abstract
This paper explores the potential of artificial intelligence, machine learning, and big data analytics in revolutionizing infection control. It addresses the challenges and innovative approaches in combating infectious diseases and antimicrobial resistance, emphasizing the critical role of interdisciplinary collaboration, ethical data practices, and integration of advanced computational tools in modern healthcare.
摘要
本文探讨了人工智能、机器学习和大数据分析在彻底改变感染控制方面的潜力。它解决了在应对传染病和抗菌药物耐药性方面的挑战和创新方法，强调了跨学科合作、伦理数据实践以及先进计算工具在现代医疗保健中的整合的关键作用。
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本文引用的文献
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Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare's Future.人工智能在应对抗菌药物耐药性方面的影响：创新、全球挑战与医疗保健的未来。
Antibiotics (Basel). 2024 May 29;13(6):502. doi: 10.3390/antibiotics13060502.
2
The impact of artificial intelligence in the fight against antimicrobial resistance.人工智能在抗击抗菌药物耐药性方面的影响。
Infect Dis (Lond). 2024 Jun;56(6):484-486. doi: 10.1080/23744235.2024.2331255. Epub 2024 Mar 21.
3
Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study.利用大型语言模型从推文内容评估传染病爆发的可能性：信息流行病学研究。
J Med Internet Res. 2024 Mar 1;26:e49139. doi: 10.2196/49139.
4
Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.电子健康记录中与医疗决策相关的自然语言处理：一项系统综述。
Comput Biol Med. 2023 Mar;155:106649. doi: 10.1016/j.compbiomed.2023.106649. Epub 2023 Feb 10.
5
Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective.机器学习在抗菌药物耐药性预测中的应用：现状、局限性和临床视角。
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6
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