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Found 6 papers, 3 papers with code
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering
Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.
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.
Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National COVID Cohort Collaborative Analysis
In this study, we defined computable phenotypes (or heuristics) and meta-heuristics for PASC phenotypes based on COVID-19 severity and symptom duration.
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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