Results 131 to 140 of about 6,023 (236)

Using machine learning algorithm to predict the risk of post-traumatic stress disorder among firefighters in Changsha. [PDF]

Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2023
Deng A, Yang Y, Li Y, Huang M, Li L, Lu Y, Chen W, Yuan R, Ju Y, Liu B, Zhang Y.   +10 more
europepmc   +1 more source

Machine learning model predicts the occurrence of acute kidney injury after open surgery for abdominal aortic aneurysm repair. [PDF]

Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2023
Sheng C, Liao M, Zhou H, Yang P.
europepmc   +1 more source

[Efficacy of machine learning models versus Cox regression model for predicting prognosis of esophagogastric junction adenocarcinoma]. [PDF]

Nan Fang Yi Ke Da Xue Xue Bao, 2023
Gao K, Wang Y, Cao H, Jia J.
europepmc   +1 more source

[An interpretable machine learning-based prediction model for risk of death for patients with ischemic stroke in intensive care unit]. [PDF]

Nan Fang Yi Ke Da Xue Xue Bao, 2023
Luo X, Cheng Y, Wu C, He J.
europepmc   +1 more source

The Research on Varying Weight Kmeans and Its Application [PDF]

, 2017
Kmeans是数据挖掘中一种比较常见的无监督机器学习方法，近年来很多学者对此进行研究，并且根据不同领域的数据特性提出了许多改进Kmeans的算法。本文也是关于Kmeans算法的研究，研究的主要问题是：一是在聚类时，所有特征变量可能受到某个暴露变量的影响从而对聚类效果的贡献不同，二是对于高维数据造成的维数灾难，如何删除冗余变量再进行聚类从而提高聚类的准确性。 针对上面两个问题，本文提出了变权重稀疏Kmeans的算法。为解决第一个问题，本文对每个特征变量对聚类效果的贡献引入了特征权重 ...
刘婉婉
core  

Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm. [PDF]

Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2023
Jiang Z, Song L, Liang C, Zhang H, Liu L.   +4 more
europepmc   +1 more source

基于机器学习的矿物智能识别方法研究进展与展望

, 2021
Huizhen Hao, Qing Gu, Xiumian Hu
openalex   +1 more source

Construction of a predictive model for radiation proctitis after radiotherapy for female pelvic tumors based on machine learning. [PDF]

Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2022
Xie H, Gong M, Zhang J, Li Q.
europepmc   +1 more source

基于IMU信号的人工智能上肢多关节运动状态识别系统构建——卒中后人工智能运动功能评估与检测系统建设前导研究 Construction of an Artificial Intelligence Upper Limb Multi-Joint Motion State Recognition System Based on IMU Signals—A Preliminary Study for the Development of an Artificial Intelligence Motor Function Assessment and Detection System after Stroke

Zhongguo cuzhong zazhi
目的 本研究旨在构建并研发一种基于低成本惯性测量单元（inertial measurement unit，IMU）信号的上肢多关节运动状态识别系统，用于快速、可靠地解码人类活动中的上肢多关节（前臂、肘关节、肩关节）运动状态，为卒中后上肢康复评估中的运动模式识别和日常运动监测提供支持。 方法 本研究纳入4名健康受试者，通过部署于手腕和上臂的IMU采集受试者的6维（3轴加速度+3轴角速度）运动信号，每名受试者重复10次。基于Fugl-Meyer运动功能评定量表上肢部分的屈肌协同运动，设计8个子任务 ...
程相鑫1，张烁1，杜松骏1，刘子阳1，周宏宇2，3，贾伟丽2，3，李子孝2，3，刘涛1 (CHENG Xiangxin1, ZHANG Shuo1, DU Songjun1, LIU Ziyang1, ZHOU Hongyu2,3, JIA Weili2,3, LI Zixiao2,3, LIU Tao1 )
doaj   +1 more source

[Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning]. [PDF]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2023
Li Z, Tian B, Liang H.
europepmc   +1 more source