改善肿瘤患者发热性中性粒细胞减少症的管理:人工智能和机器学习的作用。

IF 4.2 2区 医学 Q1 INFECTIOUS DISEASES
Antonio Gallardo-Pizarro, Olivier Peyrony, Mariana Chumbita, Patricia Monzo-Gallo, Tommaso Francesco Aiello, Christian Teijon-Lumbreras, Emmanuelle Gras, Josep Mensa, Alex Soriano, Carolina Garcia-Vidal
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引用次数: 0

摘要

导言:人工智能(AI)和机器学习(ML)有可能彻底改变发热性中性粒细胞减少症(FN)的管理,并推动个性化医疗的发展:在这篇综述中,我们详细介绍了如何通过收集大量高质量数据来利用 ML 和 AI 进行精确的数学研究。我们解释了这些技术的基础,包括有监督和无监督学习的基本原理,以及最重要的挑战,如数据质量、"黑箱 "模型解释和过度拟合。最后,我们举例详细说明了如何利用人工智能和 ML 增强对化疗引起的 FN 的预测、血流感染 (BSI) 和耐多药 (MDR) 细菌的检测,以及对严重并发症和死亡率的预测:专家观点:在管理 FN 的过程中,实施准确的人工智能和 ML 模型具有广阔的前景。然而,将其整合为可行的临床工具面临着挑战,包括技术和实施方面的障碍。提高全球可及性、促进跨学科合作以及解决伦理和安全问题至关重要。通过克服这些挑战,我们可以改变对 FN 患者的个性化护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning.

Introduction: Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine.

Areas covered: In this review, we detail how the collection of a large number of high-quality data can be used to conduct precise mathematical studies with ML and AI. We explain the foundations of these techniques, covering the fundamentals of supervised and unsupervised learning, as well as the most important challenges, e.g. data quality, 'black box' model interpretation and overfitting. To conclude, we provide detailed examples of how AI and ML have been used to enhance predictions of chemotherapy-induced FN, detection of bloodstream infections (BSIs) and multidrug-resistant (MDR) bacteria, and anticipation of severe complications and mortality.

Expert opinion: There is promising potential of implementing accurate AI and ML models whilst managing FN. However, their integration as viable clinical tools poses challenges, including technical and implementation barriers. Improving global accessibility, fostering interdisciplinary collaboration, and addressing ethical and security considerations are essential. By overcoming these challenges, we could transform personalized care for patients with FN.

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来源期刊
CiteScore
11.20
自引率
0.00%
发文量
66
审稿时长
4-8 weeks
期刊介绍: Expert Review of Anti-Infective Therapy (ISSN 1478-7210) provides expert reviews on therapeutics and diagnostics in the treatment of infectious disease. Coverage includes antibiotics, drug resistance, drug therapy, infectious disease medicine, antibacterial, antimicrobial, antifungal and antiviral approaches, and diagnostic tests.
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