Predictive modeling of biomedical temporal data in healthcare applications: review and future directions.

Predictive modeling of clinical time series data is challenging due to various factors. One such difficulty is the existence of missing values, which leads to irregular data. Another challenge is capturing correlations across multiple dimensions in order to achieve accurate predictions.

Eigen-entropy based time series signatures to support multivariate time series classification.

Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset.

Corrections to "STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation with Gait Signature Simulation and Deep Learning".

[This corrects the article PMC10399976.].

STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation with Gait Signature Simulation and Deep Learning.

Abnormal gait is a significant non-cognitive biomarker for Alzheimer's disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar, a non-wearable technology, can capture human gait movements for potential early ADRD risk assessment. In this research, we propose to design STRIDE integrating micro-Doppler radar sensors with advanced artificial intelligence (AI) technologies.