Results 101 to 110 of about 849 (155)

利用机器学习算法鉴定原发性膜性肾病的铁死亡相关诊断标志物

Zhongguo shiyan zhenduanxue
目的利用多种机器学习算法鉴定原发性膜性肾病（PMN）的铁死亡相关诊断标志物。方法从基因表达综合数据库（GEO）检索PMN患者和对照组的肾小球转录组微阵列数据，从Ferrdb数据库获取铁死亡相关基因（FRG）信息，筛选出PMN组相较于对照组差异表达的FRG（P<0.05）。采用最小绝对收缩和选择算子回归、支持向量机和随机森林3种机器学习算法鉴定出关键FRG。并分别在训练集和测试集中检测它们的差异表达情况并绘制受试者工作特征曲线（ROC）以评估其诊断效能。此外 ...
高青松, 潘婷婷, 黎丹, 袁戈, 王婷, 卢思睿, 李明   +6 more
doaj  

基于CTGAN-CRS与改进卷积神经网络的变压器故障诊断方法

Gaoya dianqi
为了提升油中溶解气体数据不平衡场景下的电力变压器故障诊断性能，文中提出了一种基于数据增强与特征扩增结合卷积神经网络进行变压器故障诊断的方法。首先，建立一种基于条件式表格生成对抗网络（conditional tabular generative adversarial network，CTGAN）结合级联式拒绝采样（cascade reject sampling，CRS）的数据增强方法，以实现不平衡数据集的高质量均衡化；其次，构建了一种全类型气体比值结合随机森林算法（gas ratios and ...
阎对丰, 刘昌林, 李元超, 王纪儒, 孔宪光   +4 more
doaj  

[Development of a machine learning-based ionization efficiency prediction model for per- and polyfluoroalkyl substances and its application in semi-quantitative analysis]. [PDF]

Se Pu
Sun SZ, Li YY, Gao Y, Li KC, Chen Z, Li XQ, Zhang QH.   +6 more
europepmc   +1 more source

[A Randomized Controlled Clinical Trial of Acupuncture at the Sphenopalatine Ganglion for the Treatment of Allergic Rhinitis]. [PDF]

Sichuan Da Xue Xue Bao Yi Xue Ban
Huang J, Zhong B, Wang F, Zhang Y, Qiu Y, Wei X, Deng J, Song H.   +7 more
europepmc   +1 more source

[Identification of high-risk preoperative blood indicators and baseline characteristics for multiple postoperative complications in rheumatoid arthritis patients undergoing total knee arthroplasty: a multi-machine learning feature contribution analysis]. [PDF]

Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi
Zhu K, Huang Z, Wang B, Li H, Wu Y, Shen B, Nie Y.   +6 more
europepmc   +1 more source

[Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning]. [PDF]

Zhejiang Da Xue Xue Bao Yi Xue Ban
Feng J, Yang Y, Liu S, Qin A, Ye J, Jiang Y.   +5 more
europepmc   +1 more source

[Risk Factors and Prognosis of Pneumoconiosis Combined With Bacterial Pneumonia: Application of a Random Forest Model]. [PDF]

Sichuan Da Xue Xue Bao Yi Xue Ban
Wang Q, Xie L, DU W, Chen M, You R, Jin Q.   +5 more
europepmc   +1 more source

[Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing]. [PDF]

Sichuan Da Xue Xue Bao Yi Xue Ban
Li H, Song Z, Zhao Y, Li M.
europepmc   +1 more source

O-RADS联合超声造影与ROMA z-score三分类预测卵巢上皮肿瘤

Zhongshan Daxue xuebao. Yixue kexue ban
目的基于O-RADS构建并评估融合超声造影（CEUS）及标准化z值的卵巢恶性肿瘤风险预测模型（ROMA z-score）的多参数诊断模型，以期提升对良性、交界性和恶性卵巢上皮性肿瘤（OETs）的三分类诊断准确性。方法本研究回顾性纳入2018年5月至2024年12月经病理确诊的129例卵巢上皮性肿瘤患者。收集患者临床资料（包括年龄、绝经状态、体质量指数、HE4、CA125）及超声影像资料（常规超声特征与超声造影特征）。根据病理结果将肿瘤分为良性、交界性及恶性三类。采用随机森林算法构建预测模型 ...
袁鲲, 朱晓敏, 张环宇, 黄羽君, 李清莹, 欧妍, 朱云晓   +6 more
doaj  

[Establishment of a Noninvasive Diagnostic Model for Wilson Disease Using Metallomics and Machine Learning]. [PDF]

Sichuan Da Xue Xue Bao Yi Xue Ban
Zhou H, Xu H, Pan A, Tang X, Zhang J, Xie L, Li Y.   +6 more
europepmc   +1 more source