# Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data

arXiv: https://arxiv.org/abs/2303.13024 · HTML: https://mlsanity.com/paper/2303.13024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series](https://mlsanity.com/paper/2107.14293/) (2021)
- [A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping](https://mlsanity.com/paper/2302.13457/) (2023)
- [Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study](https://mlsanity.com/paper/1803.09592/) (2018)

## Built on by (most cited first, 1 of 1)

- [Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering](https://mlsanity.com/paper/2401.08002/) (2024)

## Abstract (arXiv)

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. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.