• 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

Editorial. Artificial neural networks for neurosurgical diagnosis, prognosis, and management.

作者信息

Harbaugh Robert E

出版信息

Neurosurg Focus. 2018 Nov 1;45(5):E3. doi: 10.3171/2018.8.FOCUS18438.

DOI:10.3171/2018.8.FOCUS18438
PMID:30453456
Abstract
摘要

相似文献

1
Editorial. Artificial neural networks for neurosurgical diagnosis, prognosis, and management.社论。用于神经外科诊断、预后和管理的人工神经网络。
Neurosurg Focus. 2018 Nov 1;45(5):E3. doi: 10.3171/2018.8.FOCUS18438.
2
A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study.基于机器学习的方法预测成人和儿童创伤性脑损伤患者的预后和住院时间：回顾性队列研究。
J Med Internet Res. 2022 Dec 9;24(12):e41819. doi: 10.2196/41819.
3
Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach.创伤性脑损伤机械通气患者住院死亡率的预测：机器学习方法。
BMC Med Inform Decis Mak. 2020 Dec 14;20(1):336. doi: 10.1186/s12911-020-01363-z.
4
Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury.机器学习算法在预测创伤性脑损伤方面并不比回归模型表现得更好。
J Clin Epidemiol. 2020 Jun;122:95-107. doi: 10.1016/j.jclinepi.2020.03.005. Epub 2020 Mar 20.
5
Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.机器的崛起：深度学习在癌症诊断中的进展
Trends Cancer. 2019 Mar;5(3):157-169. doi: 10.1016/j.trecan.2019.02.002. Epub 2019 Feb 28.
6
The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty.人工神经网络在预测解剖型和反式肩关节置换术后住院时间、出院去向和住院费用方面的价值。
J Shoulder Elbow Surg. 2020 Nov;29(11):2385-2394. doi: 10.1016/j.jse.2020.04.009. Epub 2020 Jun 9.
7
Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury.机器学习分析在预测外伤性脑损伤患儿 6 个月结局方面优于传统统计学模型和 CT 分类系统。
Neurosurg Focus. 2018 Nov 1;45(5):E2. doi: 10.3171/2018.8.FOCUS17773.
8
Editorial. Machine learning and artificial intelligence applied to the diagnosis and management of Cushing disease.社论。机器学习与人工智能在库欣病诊断与管理中的应用
Neurosurg Focus. 2020 Jun;48(6):E6. doi: 10.3171/2020.3.FOCUS20213.
9
Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients.基于机器学习的创伤性损伤患者连续颅内压预测。
IEEE J Transl Eng Health Med. 2022 Jun 2;10:4901008. doi: 10.1109/JTEHM.2022.3179874. eCollection 2022.
10
[Prognosis Prediction of Lung Cancer Patients Using CT Images: Feature Extraction by Convolutional Neural Network and Prediction by Machine Learning].[利用CT图像预测肺癌患者的预后：通过卷积神经网络进行特征提取及机器学习预测]
Nihon Hoshasen Gijutsu Gakkai Zasshi. 2022 Aug 20;78(8):829-837. doi: 10.6009/jjrt.2022-1224. Epub 2022 Jul 8.

引用本文的文献

1
Henry gas solubility optimization double machine learning classifier for neurosurgical patients.亨利气体溶解优化双机器学习分类器，用于神经外科患者。
PLoS One. 2023 May 11;18(5):e0285455. doi: 10.1371/journal.pone.0285455. eCollection 2023.
2
Development, Validation and Comparison of Artificial Neural Network and Logistic Regression Models Predicting Eosinophilic Chronic Rhinosinusitis With Nasal Polyps.预测伴有鼻息肉的嗜酸性粒细胞性慢性鼻-鼻窦炎的人工神经网络和逻辑回归模型的开发、验证及比较
Allergy Asthma Immunol Res. 2023 Jan;15(1):67-82. doi: 10.4168/aair.2023.15.1.67.
3
The Application of Artificial Neural Networks and Logistic Regression in the Evaluation of Risk for Dry Eye after Vitrectomy.
人工神经网络和逻辑回归在玻璃体切除术后干眼风险评估中的应用
J Ophthalmol. 2020 Apr 21;2020:1024926. doi: 10.1155/2020/1024926. eCollection 2020.
4
Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study.人工神经网络可以预测创伤量和严重程度，而不受中心规模和地理位置的影响：一项多中心研究。
J Trauma Acute Care Surg. 2019 Jul;87(1):181-187. doi: 10.1097/TA.0000000000002320.