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Found 4 papers, 3 papers with code
Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data
Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure.
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping
To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time).
WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values
We also show that our Dynamic WindowSHAP algorithm focuses more on the most important time steps and provides more understandable explanations.
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury
The implemented methods were compared to one another in terms of several XAI characteristics such as understandability, fidelity, and stability.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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