Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data [0.03%]
基于脑电图数据的儿童抽动障碍及严重程度可解释机器学习模型的开发与验证
Wanting Xiang,Gang Zhu,Yichong Hou et al.
Wanting Xiang et al.
Accurate diagnosis of Tic disorders (TD) and its severity based on electroencephalogram (EEG) data were of great clinical importance. This study analyzed EEG data from 90 children with TD and 88 healthy controls (HC). A two-stage progressiv...
Integrating explainable machine learning and transcriptomics data reveals cell-type specific immune signatures underlying macular degeneration [0.03%]
可解释机器学习与转录组数据的整合揭示年龄相关黄斑变性背后细胞特异性免疫特征
Khang Ma,Hosei Nakajima,Nipa Basak et al.
Khang Ma et al.
Genome-wide association studies (GWAS) have established key role of immune dysfunction in Age-related Macular Degeneration (AMD), though the precise role of immune cells remains unclear. Here, we develop an explainable machine-learning pipe...
Predicting sorption of organic pollutants on soils with interpretable machine learning [0.03%]
基于可解释机器学习预测有机污染物在土壤中的吸附量
Qian Wang,Jianmin Bian,Enze Ma et al.
Qian Wang et al.
The sorption of organic pollutants (OPs) on soils plays a critical role in determining the environmental fate and transport of these compounds, which has been extensively studied. However, the complex nonlinear relationships between adsorpt...
Explainable machine learning model for predicting internal mammary node metastasis in breast cancer: Multi-method development and cross-cohort validation [0.03%]
一种用于预测乳腺癌内乳淋巴结转移的可解释机器学习模型：多方法开发与跨队列验证
Yirong Xiang,Jian Tie,Siyuan Zhang et al.
Yirong Xiang et al.
Background: This study developed an explainable machine learning model for baseline internal mammary lymph node metastasis (IMNM) in breast cancer patients. ...
Predicting takotsubo syndrome subtypes: An interpretable machine learning model for differentiating emotional versus physical aetiologies [0.03%]
预测 Takotsubo 综合征亚型：一种区分情绪与身体病因的可解释机器学习模型
Diego Scuppa,Francesca Colaceci,Marco Sciandrone et al.
Diego Scuppa et al.
Background: Takotsubo syndrome (TTS) is an acute coronary syndrome characterized by a reversible, mostly apical dysfunction of the left ventricle. Based on the triggering event, TTS has been classified as primary due to e...
Christina Mimikou,Christos Kokkotis,Dimitrios Tsiptsios et al.
Christina Mimikou et al.
Background: Depression constitutes a major public health issue, being one of the leading causes of the burden of disease worldwide. The risk of depression is determined by both genetic and environmental factors. While genetic factors cannot...
Explainable machine learning model predicting neurological deterioration in Wilson's disease via MRI radiomics and clinical features [0.03%]
基于MRI影像组学和临床特征预测威尔森病神经恶化的影响因素的可解释机器学习模型
Shijing Wang,Jie Chang,Caiyu Yang et al.
Shijing Wang et al.
Background: This study aims to build a machine learning (ML) model to predict the deterioration of neurological symptoms in Wilson's disease (WD) patients during short-term anti-copper therapy. The model combines brain T1...
Comparison of externally validated interpretable machine-learning model with the United States Preventive Services Task Force approach to pre-eclampsia risk assessment [0.03%]
外部验证的可解释机器学习模型与美国预防服务工作组评估子痫前期风险的方法的比较研究
T M Bosschieter,H Nori,I Painter et al.
T M Bosschieter et al.
Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning [0.03%]
基于可解释机器学习的氧氟玻璃热学和光学性质预测
Yuhao Xie,Xiangfu Wang
Yuhao Xie
Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optica...
Serum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study [0.03%]
基于血清钙的可解释机器学习模型预测结直肠癌手术后吻合口漏的多中心研究
Bo-Yu Kang,Yi-Huan Qiao,Jun Zhu et al.
Bo-Yu Kang et al.
Background: Despite the promising prospects of utilizing artificial intelligence and machine learning (ML) for comprehensive disease analysis, few models constructed have been applied in clinical practice due to their com...
Multicenter Study
World journal of gastroenterology. 2025 May 21;31(19):105283. DOI:10.3748/wjg.v31.i19.105283 2025
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