• 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

Machine learning enables update to pediatric neurorehabilitation.

作者信息

He Yunfang, Cai Simian, Peng Tingting, Qiao Yan, Wu Naiqi, Xu Kaishou

机构信息

Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology Taipa Macau China.

Department of Rehabilitation, Guangzhou Women and Children's Medical Center Guangzhou Medical University Guangzhou Guangdong China.

出版信息

Pediatr Investig. 2024 Feb 12;8(3):237-239. doi: 10.1002/ped4.12418. eCollection 2024 Sep.

DOI:10.1002/ped4.12418
PMID:39347526
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11427893/
Abstract
摘要

相似文献

1
Machine learning enables update to pediatric neurorehabilitation.机器学习助力小儿神经康复的更新。
Pediatr Investig. 2024 Feb 12;8(3):237-239. doi: 10.1002/ped4.12418. eCollection 2024 Sep.
2
Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning.基于临床数据、数字健康指标和机器学习的多发性硬化症康复结局的个体化预测：概念验证。
Med Biol Eng Comput. 2022 Jan;60(1):249-261. doi: 10.1007/s11517-021-02467-y. Epub 2021 Nov 25.
3
Is it only nice in theory? Implementation of motor learning principles in neurorehabilitation among Saudi physical therapists.在沙特物理治疗师中，运动学习原则在神经康复中的应用是否仅停留在理论上？
NeuroRehabilitation. 2023;53(3):385-395. doi: 10.3233/NRE-230071.
4
Medical tourism: evidence from an Italian descriptive survey on pediatric neurorehabilitation treatment abroad.医疗旅游：来自意大利小儿神经康复治疗海外就医描述性调查的证据。
Eur J Phys Rehabil Med. 2013 Dec;49(6):829-36. Epub 2013 Nov 4.
5
Artificial intelligence and machine learning in motor recovery: A rehabilitation medicine perspective.运动恢复中的人工智能与机器学习：康复医学视角
World J Clin Cases. 2023 Oct 16;11(29):7258-7260. doi: 10.12998/wjcc.v11.i29.7258.
6
Long-term neurorehabilitation outcomes of pediatric vs. adult onset acquired brain injury.儿童与成人后天性脑损伤的长期神经康复结果
Front Neurol. 2022 Dec 20;13:981991. doi: 10.3389/fneur.2022.981991. eCollection 2022.
7
Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.通过神经元操作条件反射实现放电同步性和振荡的意志增强：与神经康复和脑机接口的相互作用。
Front Syst Neurosci. 2014 Feb 6;8:11. doi: 10.3389/fnsys.2014.00011. eCollection 2014.
8
Coping, stress and negative psychological outcomes in parents of children admitted to a pediatric neurorehabilitation care unit.儿科神经康复护理单元患儿父母的应对方式、压力和负面心理后果。
Eur J Phys Rehabil Med. 2019 Dec;55(6):772-782. doi: 10.23736/S1973-9087.19.05695-8. Epub 2019 Jul 9.
9
Robotic neurorehabilitation: a computational motor learning perspective.机器人神经康复：计算运动学习视角
J Neuroeng Rehabil. 2009 Feb 25;6:5. doi: 10.1186/1743-0003-6-5.
10
Science-based neurorehabilitation: recommendations for neurorehabilitation from basic science.基于科学的神经康复：基础科学对神经康复的建议
J Mot Behav. 2015;47(1):7-17. doi: 10.1080/00222895.2014.931273.

引用本文的文献

1
Robot-assisted upper limb therapy for personalized rehabilitation in children with cerebral palsy: a systematic review.机器人辅助上肢治疗用于脑瘫儿童的个性化康复：一项系统综述
Front Neurol. 2025 Jan 6;15:1499249. doi: 10.3389/fneur.2024.1499249. eCollection 2024.

本文引用的文献

1
New Screening Tool for Term-Born Infants Enables Update to the Clinical Practice Guideline for Early Diagnosis of Cerebral Palsy.适用于足月儿的新型筛查工具助力更新脑瘫早期诊断临床实践指南。
JAMA Pediatr. 2023 Feb 1;177(2):115-117. doi: 10.1001/jamapediatrics.2022.5189.
2
Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk.从高危婴儿的自发性运动预测脑瘫的深度学习方法的开发和验证。
JAMA Netw Open. 2022 Jul 1;5(7):e2221325. doi: 10.1001/jamanetworkopen.2022.21325.
3
Machine learning in medical applications: A review of state-of-the-art methods.机器学习在医学应用中的应用：最新方法综述。
Comput Biol Med. 2022 Jun;145:105458. doi: 10.1016/j.compbiomed.2022.105458. Epub 2022 Mar 28.
4
Gaming Technology for Pediatric Neurorehabilitation: A Systematic Review.用于儿科神经康复的游戏技术：一项系统综述
Front Pediatr. 2022 Jan 28;10:775356. doi: 10.3389/fped.2022.775356. eCollection 2022.
5
Artificial intelligence to improve efficiency of administration of gross motor function assessment in children with cerebral palsy.人工智能提高脑瘫儿童粗大运动功能评估管理效率。
Dev Med Child Neurol. 2022 Feb;64(2):228-234. doi: 10.1111/dmcn.15010. Epub 2021 Aug 13.
6
In-depth analysis reveals complex molecular aetiology in a cohort of idiopathic cerebral palsy.深入分析揭示了特发性脑瘫患者队列中的复杂分子病因。
Brain. 2022 Mar 29;145(1):119-141. doi: 10.1093/brain/awab209.
7
Personalized machine learning for robot perception of affect and engagement in autism therapy.用于机器人在自闭症治疗中感知情感和参与度的个性化机器学习。
Sci Robot. 2018 Jun 27;3(19). doi: 10.1126/scirobotics.aao6760.
8
Artificial neural networks in neurorehabilitation: A scoping review.人工神经网络在神经康复中的应用：范围综述。
NeuroRehabilitation. 2020;46(3):259-269. doi: 10.3233/NRE-192996.
9
Multi-modular AI Approach to Streamline Autism Diagnosis in Young Children.多模块人工智能方法简化幼儿自闭症诊断
Sci Rep. 2020 Mar 19;10(1):5014. doi: 10.1038/s41598-020-61213-w.
10
Effectiveness of training with motion-controlled commercial video games on hand and arm function in young people with cerebral palsy: A systematic review and meta-analysis.使用运动控制的商业视频游戏对脑瘫青少年进行手部和手臂功能训练的有效性：一项系统评价和荟萃分析。
J Rehabil Med. 2019 Dec 3;52(1):jrm00012. doi: 10.2340/16501977-2633.