AIMC Topic: Multivariate Analysis
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Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.
Journal of medical systems
Jan 14, 2025
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into ...
Machine learning assessment of dredging impacts on the phytoplankton community on the Brazilian equatorial margin: A multivariate analysis.
Environmental pollution (Barking, Essex : 1987)
Jan 10, 2025
Dredging in estuarine systems significantly impacts phytoplankton communities, with suspended particulate matter (SPM) and dissolved aluminum (Al) serving as indicators of disturbance intensity. This study assessed the effects of dredging in the São ...
Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.
Sensors (Basel, Switzerland)
Dec 20, 2024
Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data st...
ST-Tree with interpretability for multivariate time series classification.
Neural networks : the official journal of the International Neural Network Society
Dec 3, 2024
Multivariate time series classification is of great importance in practical applications and is a challenging task. However, deep neural network models such as Transformers exhibit high accuracy in multivariate time series classification but lack int...
Estimating global phase synchronization by quantifying multivariate mutual information and detecting network structure.
Neural networks : the official journal of the International Neural Network Society
Nov 28, 2024
In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain regions. Specifically, global phase synchronization (GPS) characterizes the degree of PS among multiva...
Identification and cognitive function prediction of Alzheimer's disease based on multivariate pattern analysis of hippocampal volumes.
Journal of Alzheimer's disease : JAD
Nov 25, 2024
BACKGROUND: Alzheimer's disease (AD) is strongly associated with slowly progressive hippocampal atrophy. Elucidating the relationships between local morphometric changes and disease status for early diagnosis could be aided by machine learning algori...
TV-Net: Temporal-Variable feature harmonizing Network for multivariate time series classification and interpretation.
Neural networks : the official journal of the International Neural Network Society
Nov 14, 2024
Multivariate time series classification (MTSC), which identifies categories of multiple sensor signals recorded in continuous time, is widely used in various fields such as transportation, finance, and medical treatment. The focused challenge remains...
RFNet: Multivariate long sequence time-series forecasting based on recurrent representation and feature enhancement.
Neural networks : the official journal of the International Neural Network Society
Oct 23, 2024
Multivariate time series exhibit complex patterns and structures involving interactions among multiple variables and long-term temporal dependencies, making multivariate long sequence time series forecasting (MLSTF) exceptionally challenging. Despite...
DyGraphformer: Transformer combining dynamic spatio-temporal graph network for multivariate time series forecasting.
Neural networks : the official journal of the International Neural Network Society
Oct 17, 2024
Transformer-based models demonstrate tremendous potential for Multivariate Time Series (MTS) forecasting due to their ability to capture long-term temporal dependencies by using the self-attention mechanism. However, effectively modeling the spatial ...
Modelling multivariate spatio-temporal data with identifiable variational autoencoders.
Neural networks : the official journal of the International Neural Network Society
Oct 9, 2024
Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated from independent latent components. If these components are found, they c...
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