• 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

相似文献

1
Bringing big data to personalized healthcare: a patient-centered framework.将大数据应用于个性化医疗保健:以患者为中心的框架。
J Gen Intern Med. 2013 Sep;28 Suppl 3(Suppl 3):S660-5. doi: 10.1007/s11606-013-2455-8.
2
Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research.变革医疗服务提供:将动态模拟建模与大数据整合于卫生经济学和结果研究中。
Pharmacoeconomics. 2016 Feb;34(2):115-26. doi: 10.1007/s40273-015-0330-7.
3
The evolution of personalized healthcare and the pivotal role of European regions in its implementation.个性化医疗保健的发展以及欧洲各地区在其实施过程中的关键作用。
Per Med. 2021 May;18(3):283-294. doi: 10.2217/pme-2020-0115. Epub 2021 Apr 7.
4
The patient-centered medical home: history, components, and review of the evidence.以患者为中心的医疗之家:历史、组成部分及证据综述
Mt Sinai J Med. 2012 Jul-Aug;79(4):433-50. doi: 10.1002/msj.21326.
5
Personalized Cancer Follow-Up Care Pathways: A Delphi Consensus of Research Priorities.个性化癌症随访护理路径:德尔菲共识研究重点。
J Natl Cancer Inst. 2020 Dec 14;112(12):1183-1189. doi: 10.1093/jnci/djaa053.
6
Integrating Personalized Care Planning into Primary Care: a Multiple-Case Study of Early Adopting Patient-Centered Medical Homes.将个性化护理计划融入初级保健:早期采用以患者为中心的医疗之家的多案例研究
J Gen Intern Med. 2020 Feb;35(2):428-436. doi: 10.1007/s11606-019-05418-4. Epub 2019 Oct 24.
7
Managing customization in health care: a framework derived from the services sector literature.管理医疗保健中的定制化:一个源自服务业文献的框架。
Health Policy. 2014 Aug;117(2):216-27. doi: 10.1016/j.healthpol.2014.04.005. Epub 2014 Apr 24.
8
The patient experience of patient-centered communication with nurses in the hospital setting: a qualitative systematic review protocol.医院环境中患者与护士以患者为中心的沟通体验:一项定性系统评价方案
JBI Database System Rev Implement Rep. 2015 Jan;13(1):76-87. doi: 10.11124/jbisrir-2015-1072.
9
The patient-centered medical home neighbor: A primary care physician's view.以患者为中心的医疗之家的邻居:初级保健医生的观点。
Ann Intern Med. 2011 Jan 4;154(1):61-2. doi: 10.7326/0003-4819-154-1-201101040-00010.
10
No big data without small data: learning health care systems begin and end with the individual patient.没有小数据就没有大数据:学习型医疗保健系统始于并终于个体患者。
J Eval Clin Pract. 2015 Dec;21(6):1014-7. doi: 10.1111/jep.12350. Epub 2015 Mar 31.

引用本文的文献

1
User Character Strengths and Engagement Prediction on a Digital Mental Health Platform for Young People: Longitudinal Observational Study.青少年数字心理健康平台上的用户性格优势与参与度预测:纵向观察研究
J Med Internet Res. 2025 Aug 25;27:e73793. doi: 10.2196/73793.
2
Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study.基于自动编码器的电子健康记录中相似患者检索的表示学习:比较研究
JMIR Med Inform. 2025 Jul 24;13:e68830. doi: 10.2196/68830.
3
Low birth weight risk prediction model: a prognostic study in the Birhan field site in Ethiopia.低出生体重风险预测模型:埃塞俄比亚比尔汉实地的一项预后研究
J Glob Health. 2025 Jul 1;15:04209. doi: 10.7189/jogh.15.04209.
4
Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study.机器学习预测各种计算机断层扫描应用中的有效辐射剂量:一项虚拟人体模型研究。
Biomed Tech (Berl). 2025 Apr 8;70(4):385-391. doi: 10.1515/bmt-2024-0620. Print 2025 Aug 26.
5
Evaluation of artificial intelligence robot's knowledge and reliability on dental implants and peri-implant phenotype.人工智能机器人对牙种植体及种植体周围表型的知识和可靠性评估。
Sci Rep. 2025 Mar 19;15(1):9519. doi: 10.1038/s41598-025-94576-z.
6
Evaluation and practical application of prompt-driven ChatGPTs for EMR generation.用于电子病历生成的提示驱动型ChatGPT的评估与实际应用
NPJ Digit Med. 2025 Feb 2;8(1):77. doi: 10.1038/s41746-025-01472-x.
7
Barriers impeding research data sharing on chronic disease prevention among the older adults in low-and middle-income countries: a systematic review.低收入和中等收入国家老年人慢性病预防研究数据共享的障碍:一项系统综述
Front Public Health. 2024 Nov 29;12:1437543. doi: 10.3389/fpubh.2024.1437543. eCollection 2024.
8
Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.用于预测血液透析患者全因死亡率和死亡时间的可解释机器学习模型。
Ther Apher Dial. 2025 Apr;29(2):220-232. doi: 10.1111/1744-9987.14212. Epub 2024 Sep 26.
9
Differences in changes of data completeness after the implementation of an electronic medical record in three surgical departments of a German hospital-a longitudinal comparative document analysis.德国某医院三个外科部门实施电子病历后数据完整性变化的差异——一项纵向比较文献分析。
BMC Med Inform Decis Mak. 2024 Sep 16;24(1):258. doi: 10.1186/s12911-024-02667-0.
10
The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.深度学习和机器学习在纵向电子健康记录中用于疾病的早期检测和预防的应用:范围综述。
J Med Internet Res. 2024 Aug 20;26:e48320. doi: 10.2196/48320.

本文引用的文献

1
Exploring and exploiting disease interactions from multi-relational gene and phenotype networks.从多关系基因和表型网络中探索和利用疾病相互作用。
PLoS One. 2011;6(7):e22670. doi: 10.1371/journal.pone.0022670. Epub 2011 Jul 29.
2
Molecular networks as sensors and drivers of common human diseases.作为常见人类疾病的传感器和驱动因素的分子网络。
Nature. 2009 Sep 10;461(7261):218-23. doi: 10.1038/nature08454.
3
Motion tracking on elbow tissue from ultrasonic image sequence for patients with lateral epicondylitis.针对患有外侧上髁炎的患者,对超声图像序列中的肘部组织进行运动跟踪。
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:95-8. doi: 10.1109/IEMBS.2007.4352231.
4
Association studies in an era of too much information: clinical analysis of new biomarker and genetic data.信息过载时代的关联研究:新生物标志物与基因数据的临床分析
Circulation. 2007 Oct 23;116(17):1866-70. doi: 10.1161/CIRCULATIONAHA.107.741611.
5
Network medicine--from obesity to the "diseasome".网络医学——从肥胖到“疾病组”
N Engl J Med. 2007 Jul 26;357(4):404-7. doi: 10.1056/NEJMe078114. Epub 2007 Jul 25.
6
Human disease classification in the postgenomic era: a complex systems approach to human pathobiology.后基因组时代的人类疾病分类:人类病理生物学的复杂系统方法
Mol Syst Biol. 2007;3:124. doi: 10.1038/msb4100163. Epub 2007 Jul 10.
7
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.对14000例七种常见疾病患者及3000例共享对照进行全基因组关联研究。
Nature. 2007 Jun 7;447(7145):661-78. doi: 10.1038/nature05911.
8
Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors.通过对临床因素进行人工神经网络分析预测丙型肝炎病毒感染的肝移植受者的显著纤维化
Eur J Gastroenterol Hepatol. 2006 Dec;18(12):1255-61. doi: 10.1097/01.meg.0000243885.55562.7e.
9
Prospective health care: the second transformation of medicine.前瞻性医疗保健:医学的第二次变革。
Genome Biol. 2006;7(2):104. doi: 10.1186/gb-2006-7-2-104. Epub 2006 Feb 27.
10
Mortality after the hospitalization of a spouse.配偶住院后的死亡率。
N Engl J Med. 2006 Feb 16;354(7):719-30. doi: 10.1056/NEJMsa050196.

将大数据应用于个性化医疗保健:以患者为中心的框架。

Bringing big data to personalized healthcare: a patient-centered framework.

出版信息

J Gen Intern Med. 2013 Sep;28 Suppl 3(Suppl 3):S660-5. doi: 10.1007/s11606-013-2455-8.

DOI:10.1007/s11606-013-2455-8
PMID:23797912
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3744281/
Abstract

Faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the full benefits of personal health and healthcare-related data. Imagine visiting your physician's office with a list of concerns and questions. What if you could walk out the office with a personalized assessment of your health? What if you could have personalized disease management and wellness plan? These are the goals and vision of the work discussed in this paper. The timing is right for such a research direction--given the changes in health care, reimbursement, reform, meaningful use of electronic health care data, and patient-centered outcome mandate. We present the foundations of work that takes a Big Data driven approach towards personalized healthcare, and demonstrate its applicability to patient-centered outcomes, meaningful use, and reducing re-admission rates.

摘要

面对不可持续的成本和大量未充分利用的数据,医疗保健需要更有效的实践、研究和工具,以充分利用个人健康和医疗保健相关数据的优势。想象一下,你带着一系列担忧和问题去看医生的办公室。如果你能在离开办公室时得到一份个性化的健康评估,那会怎样?如果你能拥有个性化的疾病管理和健康计划呢?这些都是本文所讨论工作的目标和愿景。考虑到医疗保健、报销、改革、电子医疗保健数据的有意义使用以及以患者为中心的结果要求的变化,现在正是开展这种研究方向的时机。我们介绍了一种采用大数据驱动方法实现个性化医疗保健的工作基础,并展示了它在以患者为中心的结果、有意义的使用和降低再入院率方面的适用性。