Projects per year
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 language | English |
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Title of host publication | 2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings |
Editors | Marco Gori, Alessandro Sperduti |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Number of pages | 8 |
ISBN (Electronic) | 9781728186719 |
ISBN (Print) | 9781665495264 |
DOIs | |
Publication status | Published - 2022 |
Event | IEEE International Joint Conference on Neural Networks 2022 - Padua, Italy Duration: 18 Jul 2022 → 23 Jul 2022 https://ieeexplore.ieee.org/xpl/conhome/9891857/proceeding (Proceedings) |
Publication series
Name | Proceedings of the International Joint Conference on Neural Networks |
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Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Volume | 2022-July |
ISSN (Print) | 2161-4393 |
ISSN (Electronic) | 2161-4407 |
Conference
Conference | IEEE International Joint Conference on Neural Networks 2022 |
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Abbreviated title | IJCNN 2022 |
Country/Territory | Italy |
City | Padua |
Period | 18/07/22 → 23/07/22 |
Internet address |
Keywords
- clinical psychology
- deep learning
- risk assessment
- time series
Projects
- 1 Finished
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Efficient and effective analytics for real-world time series forecasting
Bergmeir, C.
Australian Research Council (ARC)
18/02/19 → 31/12/22
Project: Research