Predicting hypotensive episodes in the traumatic brain injury domain

The domain with which this research is concerned is traumatic brain injury and models which attempt to predict hypotensive (low blood pressure) events occurring in a hospital intensive care unit environment. The models process anonymised, clinical, minute-byminute, physiological data from the BrainIT consortium. The research reviews three predictive modelling techniques: classic time series analysis; hidden Markov models; and classifier models, which are the main focus of this thesis. The data preparation part of this project is extensive and six applications have been developed: an event list generator, used to process a given event definition; a data set generation tool, which produces a series of base data sets that can be used to train machine learning models; a training and test set generation application, which produces randomly drawn training and test data sets; an application used to build and assess a series of logistic regression models; an application to test the statistical models on unseen data, which uses anonymised real clinical data from intensive care unit bedside monitors; and finally, an application that implements a proposed clinical warning protocol, which attempts to assess a model’s performance in terms of usefulness to a clinical team. These applications are being made available under a public domain licence to enable further research (see Appendix A for details). Six logistic regression models and two Bayesian neural network models are examined using the physiological signals heart rate and arterial blood pressure, along with the demographic variables of age and gender. Model performance is assessed using the standard ROC technique to give the AUC metric. An alternative performance metric, the H score, is also investigated. Using unseen clinical data, two of the models are assessed in a manner which mimics the ICU environment. This approach shows that models may perform better than would be suggested by standard assessment metrics. The results of the modelling experiments are compared with a recent similar project in the healthcare domain and show that logistic regression models could form the basis of a practical early warning system for use in a neuro intensive care unit