Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data.
Hamid GhaderiBrandon ForemanAmin NayebiSindhu TipirneniChandan K ReddyVignesh SubbianPublished in: AMIA ... Annual Symposium proceedings. AMIA Symposium (2024)
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.
Keyphrases
- traumatic brain injury
- respiratory failure
- electronic health record
- heart failure
- big data
- data analysis
- liver failure
- machine learning
- single cell
- deep learning
- rna seq
- case report
- severe traumatic brain injury
- extracorporeal membrane oxygenation
- healthcare
- mechanical ventilation
- physical activity
- drug induced
- artificial intelligence
- neural network
- intensive care unit
- hepatitis b virus
- left ventricular
- acute respiratory distress syndrome