# RNN-BOF: A Multivariate Global Recurrent Neural Network for Binary Outcome Forecasting of Inpatient Aggression

arXiv: https://arxiv.org/abs/2312.01029 · HTML: https://mlsanity.com/paper/2312.01029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/1905.03806/) (2019)
- [Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality](https://mlsanity.com/paper/2008.00444/) (2020)
- [Global Models for Time Series Forecasting: A Simulation Study](https://mlsanity.com/paper/2012.12485/) (2020)
- [Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology](https://mlsanity.com/paper/1901.04028/) (2019)

## Abstract (arXiv)

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.