• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

The Clinician's Guide to the Machine Learning Galaxy.

作者信息

Shen Lin, Kann Benjamin H, Taylor R Andrew, Shung Dennis L

机构信息

Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States.

Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Boston, MA, United States.

出版信息

Front Physiol. 2021 Apr 6;12:658583. doi: 10.3389/fphys.2021.658583. eCollection 2021.

DOI:10.3389/fphys.2021.658583
PMID:33889088
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8056037/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd9a/8056037/1fc944b4a4cf/fphys-12-658583-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd9a/8056037/1fc944b4a4cf/fphys-12-658583-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd9a/8056037/1fc944b4a4cf/fphys-12-658583-g0001.jpg

相似文献

1
The Clinician's Guide to the Machine Learning Galaxy.《临床医生机器学习指南》
Front Physiol. 2021 Apr 6;12:658583. doi: 10.3389/fphys.2021.658583. eCollection 2021.
2
Supervised Machine Learning in Oncology: A Clinician's Guide.肿瘤学中的监督式机器学习：临床医生指南
Dig Dis Interv. 2020 Mar;4(1):73-81. doi: 10.1055/s-0040-1705097.
3
Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective.机器学习在预测放射治疗结果中的应用：临床医生的视角。
Int J Radiat Oncol Biol Phys. 2015 Dec 1;93(5):1127-35. doi: 10.1016/j.ijrobp.2015.07.2286. Epub 2015 Nov 11.
4
Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence.报告和实施涉及机器学习和人工智能的干预措施。
Ann Intern Med. 2020 Jun 2;172(11 Suppl):S137-S144. doi: 10.7326/M19-0872.
5
How Bioethics Can Shape Artificial Intelligence and Machine Learning.生物伦理学如何塑造人工智能和机器学习
Hastings Cent Rep. 2018 Sep;48(5):10-13. doi: 10.1002/hast.895.
6
[Applications of machine learning in clinical decision support in the omic era].[机器学习在组学时代临床决策支持中的应用]
Yi Chuan. 2018 Sep 20;40(9):693-703. doi: 10.16288/j.yczz.18-139.
7
Artificial intelligence in neurosciences: A clinician's perspective.神经科学中的人工智能：临床医生的视角。
Neurol India. 2018 Jul-Aug;66(4):934-939. doi: 10.4103/0028-3886.236971.
8
Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success.机器学习和人工智能在神经危重症护理中的应用：是一种全专业范围的颠覆性变革，还是一种成功策略。
Curr Neurol Neurosci Rep. 2019 Nov 13;19(11):89. doi: 10.1007/s11910-019-0998-8.
9
Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?机器学习方法能否像临床医生一样预测早产儿的拔管结果？
J Neonatal Biol. 2013;2. doi: 10.4172/2167-0897.1000118.
10
Advancing care for acute gastrointestinal bleeding using artificial intelligence.利用人工智能提升急性胃肠道出血的治疗水平。
J Gastroenterol Hepatol. 2021 Feb;36(2):273-278. doi: 10.1111/jgh.15372.

引用本文的文献

1
Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.用于支持肿瘤学中患者相关决策的经过外部验证且具有临床实用性的机器学习算法：一项范围综述。
BMC Med Res Methodol. 2025 Feb 21;25(1):45. doi: 10.1186/s12874-025-02463-y.
2
Application of machine learning in acute upper gastrointestinal bleeding: bibliometric analysis.机器学习在急性上消化道出血中的应用：文献计量分析
Front Med (Lausanne). 2024 Nov 18;11:1490757. doi: 10.3389/fmed.2024.1490757. eCollection 2024.
3
Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Estimating Sample Size and Reducing Overfitting.

本文引用的文献

1
Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis.美国和欧洲对人工智能和基于机器学习的医疗器械的审批（2015-20）：比较分析。
Lancet Digit Health. 2021 Mar;3(3):e195-e203. doi: 10.1016/S2589-7500(20)30292-2. Epub 2021 Jan 18.
2
Artificial intelligence: opportunities and risks for public health.人工智能：公共卫生的机遇与风险。
Lancet Digit Health. 2019 May;1(1):e13-e14. doi: 10.1016/S2589-7500(19)30002-0. Epub 2019 May 2.
3
Unravelling the effect of data augmentation transformations in polyp segmentation.
迈向语音、语言和听力科学中的通用机器学习模型：估计样本量并减少过拟合。
J Speech Lang Hear Res. 2024 Mar 11;67(3):753-781. doi: 10.1044/2023_JSLHR-23-00273. Epub 2024 Feb 22.
揭示数据增强变换在息肉分割中的作用。
Int J Comput Assist Radiol Surg. 2020 Dec;15(12):1975-1988. doi: 10.1007/s11548-020-02262-4. Epub 2020 Sep 28.
4
The Case for Algorithmic Stewardship for Artificial Intelligence and Machine Learning Technologies.人工智能和机器学习技术的算法管理理由
JAMA. 2020 Oct 13;324(14):1397-1398. doi: 10.1001/jama.2020.9371.
5
Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms.隐匿于众目睽睽之下——重新审视临床算法中种族校正的应用
N Engl J Med. 2020 Aug 27;383(9):874-882. doi: 10.1056/NEJMms2004740. Epub 2020 Jun 17.
6
How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening.人工智能将如何影响结肠镜检查和结直肠癌筛查
Gastrointest Endosc Clin N Am. 2020 Jul;30(3):585-595. doi: 10.1016/j.giec.2020.02.010. Epub 2020 Apr 11.
7
Expert-augmented machine learning.专家增强型机器学习。
Proc Natl Acad Sci U S A. 2020 Mar 3;117(9):4571-4577. doi: 10.1073/pnas.1906831117. Epub 2020 Feb 18.
8
Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial.随机试验中 CT 容积筛查降低肺癌死亡率
N Engl J Med. 2020 Feb 6;382(6):503-513. doi: 10.1056/NEJMoa1911793. Epub 2020 Jan 29.
9
Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study.使用实时计算机辅助系统（ENDOANGEL）检测结直肠腺瘤：一项随机对照研究。
Lancet Gastroenterol Hepatol. 2020 Apr;5(4):352-361. doi: 10.1016/S2468-1253(19)30413-3. Epub 2020 Jan 22.
10
Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.深度学习计算机辅助检测系统对结肠镜检查中腺瘤检测的影响（CADe-DB 试验）：一项双盲随机研究。
Lancet Gastroenterol Hepatol. 2020 Apr;5(4):343-351. doi: 10.1016/S2468-1253(19)30411-X. Epub 2020 Jan 22.