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Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion

机译：检查人工神经网络机器学习模型准确预测后路腰椎融合术后并发症的能力

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摘要

Study Design.A cross-sectional database study.

机译：研究设计。横断面数据库研究。

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期刊名称 other
作者
Jun S. Kim; Robert K. Merrill; Varun Arvind; Deepak Kaji; Sara D. Pasik; Chuma C. Nwachukwu; Luilly Vargas; Nebiyu S. Osman; Eric K. Oermann; John M. Caridi; Samuel K. Cho;
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年(卷),期 -1(43),12
年度 -1
页码 853–860
总页数 17
原文格式 PDF
正文语种
中图分类
关键词
AI ANN artificial intelligence artificial neural networks complications logistic regression LR machine learning neural nets PLF posterior lumbar fusion prediction;

机译：AI;ANN;人工智能;人工神经网络;并发症;逻辑回归;LR;机器学习;神经网络;PLF;后腰椎融合;预测;

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专利

1. Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion [J] . Kim Jun S., Merrill Robert K., Arvind Varun, Spine . 2018,第12期

机译：检查人工神经网络机器学习模型的能力，准确预测后腰椎脊柱融合后的并发症
2. Can a machine learning model accurately predict patient resource utilization following lumbar spinal fusion? [J] . Karnuta Jaret M., Golubovsky Joshua L., Haeberle Heather S., The spine journal: official journal of the North American Spine Society . 2020,第3期

机译：机器学习模型可以准确地预测腰椎脊柱融合后的患者资源利用吗？
3. A Machine-Learning Model Using Artificial Neural Network to Facilitate Liver SBRT Prescription Selection By Predicting Normal Liver Geud Based on Geometric Properties of Liver and PTV [J] . Wang Y. International Journal of Radiation Oncology, Biology, Physics . 2019,第1Suppla期

机译：一种利用人工神经网络的机器学习模型，以促进肝脏SBRT处方选择，通过肝脏和PTV几何特性预测正常肝geud
4. MODELING AND ANALYSIS OF THE EFFECT OF DISC DEGENERATION, ARTIFICIAL DISC REPLACEMENT AND SPINAL FUSION ON THE BIOMECHANICS OF THE LUMBAR SPINE [C] . Andre P. Mazzoleni, Antonio Gonzalez, David Padgett, IMECE2008;ASME international mechanical engineering congress and exposition . 2009

机译：椎间盘退变，人工椎间盘置换和脊髓融合对腰椎生物力学影响的建模与分析
5. Developing an accurate forecasting model for temporal and spatial ambulance demand via artificial neural networks: A comparative study of existing forecasting techniques vs. an artificial neural network. [D] . Setzler, Hubert Holland, III. 2007

机译：通过人工神经网络为时空救护车需求开发准确的预测模型：现有预测技术与人工神经网络的比较研究。
6. Predictive analysis of urban waste generation for the city of Bogotá Colombia through the implementation of decision trees-based machine learning support vector machines and artificial neural networks [O] . Johanna Karina Solano Meza, David Orjuela Yepes, Javier Rodrigo-Ilarri, 2019

机译：通过实施基于决策树的机器学习支持向量机和人工神经网络对哥伦比亚波哥大市的城市垃圾产生进行预测分析
7. Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion [O] . Jun S. Kim, Robert K. Merrill, Varun Arvind, 2018

机译：检查人工神经网络机器学习模型的能力，准确预测后腰椎脊柱融合后的并发症
8. Least-Squares Learning and Approximation of Posterior Probabilities on Classification Problems by Neural Network Models. [R] . Shoemaker, P. A., Carlin, M. J., Shimabukuro, R. L., 1991

机译：基于神经网络模型的分类问题最小二乘学习及后验概率逼近。

Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion

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