RNN-BOF: a multivariate global recurrent neural network for binary outcome forecasting of inpatient aggression

Aidan Quinn, Melanie Simmons, Benjamin Spivak, Christoph Bergmeir

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings
EditorsMarco Gori, Alessandro Sperduti
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728186719
ISBN (Print)9781665495264
DOIs
Publication statusPublished - 2022
EventIEEE International Joint Conference on Neural Networks 2022 - Padua, Italy
Duration: 18 Jul 202223 Jul 2022
https://ieeexplore.ieee.org/xpl/conhome/9891857/proceeding (Proceedings)

Publication series

NameProceedings of the International Joint Conference on Neural Networks
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2022-July
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

ConferenceIEEE International Joint Conference on Neural Networks 2022
Abbreviated titleIJCNN 2022
Country/TerritoryItaly
CityPadua
Period18/07/2223/07/22
Internet address

Keywords

  • clinical psychology
  • deep learning
  • risk assessment
  • time series

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