RNN-BOF: A Multivariate Global Recurrent Neural Network for Binary
  Outcome Forecasting of Inpatient Aggression
Aidan Quinn Melanie Simmons Benjamin Spivak Christoph Bergmeir
Psychometric assessment instruments aid clinicians by providing methods of
assessing the future risk of adverse events such as aggression. Existing
machine learning approaches have treated this as a classification problem,
predicting the probability of an adverse event in a fixed future time period
from the scores produced by both psychometric instruments and clinical and
demographic covariates. We instead propose modelling a patient's future risk
using a time series methodology that learns from longitudinal data and produces
a probabilistic binary forecast that indicates the presence of the adverse
event in the next time period. Based on the recent success of Deep Neural Nets
for globally forecasting across many time series, we introduce a global
multivariate Recurrent Neural Network for Binary Outcome Forecasting, that
trains from and for a population of patient time series to produce individual
probabilistic risk assessments. We use a moving window training scheme on a
real world dataset of 83 patients, where the main binary time series represents
the presence of aggressive events and covariate time series represent clinical
or demographic features and psychometric measures. On this dataset our approach
was capable of a significant performance increase against both benchmark
psychometric instruments and previously used machine learning methodologies.