机器学习能否为败血症患者提供个性化心血管治疗?

Q4 Medicine
Critical care explorations Pub Date : 2024-05-06 eCollection Date: 2024-05-01 DOI:10.1097/CCE.0000000000001087
Finneas J R Catling, Myura Nagendran, Paul Festor, Zuzanna Bien, Steve Harris, A Aldo Faisal, Anthony C Gordon, Matthieu Komorowski
{"title":"机器学习能否为败血症患者提供个性化心血管治疗?","authors":"Finneas J R Catling, Myura Nagendran, Paul Festor, Zuzanna Bien, Steve Harris, A Aldo Faisal, Anthony C Gordon, Matthieu Komorowski","doi":"10.1097/CCE.0000000000001087","DOIUrl":null,"url":null,"abstract":"<p><p>Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11075946/pdf/","citationCount":"0","resultStr":"{\"title\":\"Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?\",\"authors\":\"Finneas J R Catling, Myura Nagendran, Paul Festor, Zuzanna Bien, Steve Harris, A Aldo Faisal, Anthony C Gordon, Matthieu Komorowski\",\"doi\":\"10.1097/CCE.0000000000001087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements.</p>\",\"PeriodicalId\":93957,\"journal\":{\"name\":\"Critical care explorations\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11075946/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Critical care explorations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1097/CCE.0000000000001087\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical care explorations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/CCE.0000000000001087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

摘要

针对脓毒症的大型随机试验通常无法找到有效的新疗法。这越来越多地归因于患者的异质性,包括脓毒性休克中心血管的异质性变化。我们讨论了机器学习系统在个性化脓毒症心血管复苏方面的潜力。虽然文献中不乏概念证明,但当前系统的技术准备程度较低,临床试验和经证实的患者获益也很少。系统可能容易受到混杂因素的影响,也不能很好地推广到新的患者群体或现代护理模式中。典型的电子健康记录无法以足够的时间分辨率获取足够丰富的数据,因此无法生成可提出可行治疗建议的系统。为了解决这些问题,我们建议同时关注技术挑战和消除转化障碍。这将涉及提高数据质量、采用因果关系模型、优先考虑安全评估和整合到医疗保健工作流程中、开展随机临床试验以及与监管要求保持一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements.

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
5.70
自引率
0.00%
发文量
0
审稿时长
8 weeks
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信