Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning [0.03%]
基于Braak分期淀粉样蛋白-β生物标志物和机器学习预测认知障碍及区域中枢
Puskar Bhattarai,Ahmed Taha,Bhavin Soni et al.
Puskar Bhattarai et al.
Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. I...
Effect of data harmonization of multicentric dataset in ASD/TD classification [0.03%]
自闭症谱系障碍/典型发育多中心数据集的数据规范化对其分类效果的影响分析
Giacomo Serra,Francesca Mainas,Bruno Golosio et al.
Giacomo Serra et al.
Machine Learning (ML) is nowadays an essential tool in the analysis of Magnetic Resonance Imaging (MRI) data, in particular in the identification of brain correlates in neurological and neurodevelopmental disorders. ML requires datasets of ...
Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer's disease [0.03%]
随机生存森林在预测轻度认知障碍转化为阿尔茨海默病风险中的可解释性
Alessia Sarica,Federica Aracri,Maria Giovanna Bianco et al.
Alessia Sarica et al.
Random Survival Forests (RSF) has recently showed better performance than statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk from mild cognitive impairment (MCI) to Alzheimer's disease (AD). However,...
Semantic representation of neural circuit knowledge in Caenorhabditis elegans [0.03%]
秀丽隐杆线虫神经环路知识的语义表示
Sharan J Prakash,Kimberly M Van Auken,David P Hill et al.
Sharan J Prakash et al.
In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approac...
Lena Kopnarski,Laura Lippert,Julian Rudisch et al.
Lena Kopnarski et al.
In order to grasp and transport an object, grip and load forces must be scaled according to the object's properties (such as weight). To select the appropriate grip and load forces, the object weight is estimated based on experience or, in ...
Attribute Selection Hybrid Network Model for risk factors analysis of postpartum depression using Social media [0.03%]
基于社交平台的产后抑郁风险因素分析属性选择混合网络模型
Abinaya Gopalakrishnan,Raj Gururajan,Revathi Venkataraman et al.
Abinaya Gopalakrishnan et al.
Background and objective: Postpartum Depression (PPD) is a frequently ignored birth-related consequence. Social network analysis can be used to address this issue because social media network serves as a platform for thei...
Investigating the mental health of university students during the COVID-19 pandemic in a UK university: a machine learning approach using feature permutation importance [0.03%]
英国大学新冠肺炎疫情期间大学生心理状况的调查研究——一种基于特征排列的重要性机器学习方法
Tianhua Chen
Tianhua Chen
Mental wellbeing of university students is a growing concern that has been worsening during the COVID-19 pandemic. Numerous studies have gathered empirical data to explore the mental health impact of the pandemic on university students and ...
Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment [0.03%]
基于深度学习的术后胶质母细胞瘤MRI分割算法：肿瘤负荷评估的新工具
Andrea Bianconi,Luca Francesco Rossi,Marta Bonada et al.
Andrea Bianconi et al.
Objective: Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinic...
Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals [0.03%]
基于原始不平衡EEG信号的自激精神病理障碍识别Transformer模型
Neha Gour,Taimur Hassan,Muhammad Owais et al.
Neha Gour et al.
Early identification of mental disorders, based on subjective interviews, is extremely challenging in the clinical setting. There is a growing interest in developing automated screening tools for potential mental health problems based on bi...
Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions [0.03%]
用于慈爱冥想EEG分类的共同空间模式：单次和多次实验的有效性研究
Nalinda D Liyanagedera,Ali Abdul Hussain,Amardeep Singh et al.
Nalinda D Liyanagedera et al.
While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus,...