Recurrent Neural Network based Time-Series Modeling for Long-term Prognosis Following Acute Traumatic Brain Injury

Amin Nayebi, Sindhu Tipirneni, Brandon Foreman, Jonathan Ratcliff, Chandan K. Reddy, Vignesh Subbian

Research output: Contribution to journalArticlepeer-review

Abstract

We developed a prognostic model for longer-term outcome prediction in traumatic brain injury (TBI) using an attention-based recurrent neural network (RNN). The model was trained on admission and time series data obtained from a multi-site, longitudinal, observational study of TBI patients. We included 110 clinical variables as model input and Glasgow Outcome Score Extended (GOSE) at six months after injury as the outcome variable. Designed to handle missing values in time series data, the RNN model was compared to an existing TBI prognostic model using 10-fold cross validation. The area under receiver operating characteristic curve (AUC) for the RNN model is 0.86 (95% CI 0.83-0.89) for binary outcomes, whereas the AUC of the comparison model is 0.69 (95% CI 0.67-0.71). We demonstrated that including time series data into prognostic models for TBI can boost the discriminative ability of prediction models with either binary or ordinal outcomes.

Original languageEnglish (US)
Pages (from-to)900-909
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2021
StatePublished - 2021

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint

Dive into the research topics of 'Recurrent Neural Network based Time-Series Modeling for Long-term Prognosis Following Acute Traumatic Brain Injury'. Together they form a unique fingerprint.

Cite this