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 Expert Review of Anti-infective Therapy Pub Date : 2024-04-01 Epub Date: 2024-03-08 DOI:10.1080/14787210.2024.2322445
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
{"title":"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。","authors":"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","doi":"10.1080/14787210.2024.2322445","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine.</p><p><strong>Areas covered: </strong>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.</p><p><strong>Expert opinion: </strong>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.</p>","PeriodicalId":12213,"journal":{"name":"Expert Review of Anti-infective Therapy","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning.\",\"authors\":\"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\",\"doi\":\"10.1080/14787210.2024.2322445\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine.</p><p><strong>Areas covered: </strong>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.</p><p><strong>Expert opinion: </strong>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.</p>\",\"PeriodicalId\":12213,\"journal\":{\"name\":\"Expert Review of Anti-infective Therapy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Expert Review of Anti-infective Therapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/14787210.2024.2322445\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/3/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Review of Anti-infective Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/14787210.2024.2322445","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/8 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
The opportunities and challenges of epigenetic approaches to manage herpes simplex infections. Potential activity of nanomaterials to combat SARS-CoV-2 and mucormycosis ‎coinfection‎. Clinical effectiveness of oral antivirals for non-hospitalized adult COVID-19 patients aged 18-60 years. Is self-medication with antibiotics among the public a global concern: a mixed-methods systematic review. Continuous care engagement in clinical practice: perspectives on selected current strategies for people with HIV in the United States.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1