Enhancing cerebral infarct classification by automatically extracting relevant fMRI features [0.03%]
通过自动提取相关fMRI特征来增强脑梗死分类
Vitaly I Dobromyslin,Wenjin Zhou;Alzheimer’s Disease Neuroimaging Initiative
Vitaly I Dobromyslin
Accurate detection of cortical infarct is critical for timely treatment and improved patient outcomes. Current brain imaging methods often require invasive procedures that primarily assess blood vessel and structural white matter damage. Th...
Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence [0.03%]
使用可解释的人工智能检测纵向阿尔茨海默病数据中的标签噪声
Paolo Sorino,Angela Lombardi,Domenico Lofù et al.
Paolo Sorino et al.
Reliable classification of cognitive states in longitudinal Alzheimer's Disease (AD) studies is critical for early diagnosis and intervention. However, inconsistencies in diagnostic labeling, arising from subjective assessments, evolving cl...
AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts [0.03%]
一种基于人工智能的多智能体强化学习框架用于压力和抑郁情境下生理信号的实时监控
Thanveer Shaik,Xiaohui Tao,Lin Li et al.
Thanveer Shaik et al.
Purpose: Effective patient monitoring is crucial for timely healthcare interventions and improved outcomes, especially in managing conditions influenced by stress and depression, which can manifest through physiological c...
Advancing Alzheimer's disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis [0.03%]
基于EEG数据和片段长度分析的新型卷积神经网络框架助力阿尔茨海默病检测研究
Md Nurul Ahad Tawhid,Siuly Siuly,Enamul Kabir et al.
Md Nurul Ahad Tawhid et al.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects memory, thinking, and behavior, leading to dementia, a severe cognitive decline. While no cure currently exists, recent advancements in preventive d...
Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder [0.03%]
采用新型核k均值机器学习算法对双相I型障碍保险理赔的治疗路径聚类：一项回顾性分析
Matthew Littman,Huy-Binh Nguyen,Joanna Campbell et al.
Matthew Littman et al.
In real-world psychiatric practice, patients may experience complex treatment journeys, including various diagnoses and lines of therapy. Insurance claims databases could potentially provide insight into outcomes of psychiatric treatment pr...
HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers [0.03%]
用于保护阅读障碍神经生物标志物隐私的高效全同态残数系统卷积神经网络模型
Opeyemi Lateef Usman,Ravie Chandren Muniyandi,Khairuddin Omar et al.
Opeyemi Lateef Usman et al.
Recent advancements in cloud-based machine learning (ML) now allow for the rapid and remote identification of neural-biomarkers associated with common neuro-developmental disorders from neuroimaging datasets. Due to the sensitive nature of ...
Explainable CNN for brain tumor detection and classification through XAI based key features identification [0.03%]
基于XAI的键特征识别的可解释CNN脑肿瘤检测与分类方法
Shagufta Iftikhar,Nadeem Anjum,Abdul Basit Siddiqui et al.
Shagufta Iftikhar et al.
Despite significant advancements in brain tumor classification, many existing models suffer from complex structures that make them difficult to interpret. This complexity can hinder the transparency of the decision-making process, causing m...
Breakdown of the compositional data approach in psychometric Likert scale big data analysis: about the loss of statistical power of two-sample t-tests applied to heavy-tailed big data [0.03%]
心理计量利克特量表大数据分析中组成数据方法的局限性：两样本t检验在重尾大数据中的统计功效丧失
René Lehmann,Bodo Vogt
René Lehmann
Bipolar psychometric scale data play a crucial role in psychological healthcare and health economics, such as in psychotherapeutic profiling and setting standards. Creating an accurate psychological profile not only benefits the patient but...
Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation [0.03%]
使用神经影像数据对阿尔茨海默病诊断进行机器学习建模：综述、可重复性及泛化能力评估
Maryam Akhavan Aghdam,Serdar Bozdag,Fahad Saeed;Alzheimer’s Disease Neuroimaging Initiative
Maryam Akhavan Aghdam
Clinical diagnosis of Alzheimer's disease (AD) is usually made after symptoms such as short-term memory loss are exhibited, which minimizes the intervention and treatment options. The existing screening techniques cannot distinguish between...
Exploring multi-granularity balance strategy for class incremental learning via three-way granular computing [0.03%]
基于三支粒计算的类增量学习多粒度平衡策略研究
Yan Xian,Hong Yu,Ye Wang et al.
Yan Xian et al.
Class incremental learning (CIL) is a specific scenario in incremental learning. It aims to continuously learn new classes from the data stream, which suffers from the challenge of catastrophic forgetting. Inspired by the human hippocampus,...