Projects per year
Abstract
Time series models such as dynamical systems are frequently fitted to a cohort of data, ignoring variation between individual entities such as patients. In this paper we show how these models can be personalised to an individual level while retaining statistical power, via use of multi-task learning (MTL). To our knowledge this is a novel development of MTL which applies to time series both with and without control inputs. The modelling framework is demonstrated on a physiological drug response problem which results in improved predictive accuracy and uncertainty estimation over existing state-of-the-art models.
Original language | English |
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Title of host publication | Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics |
Editors | Neil Lawrence, Mark Reid |
Publisher | PMLR |
Number of pages | 10 |
Volume | 89 |
Publication status | E-pub ahead of print - 18 Apr 2019 |
Event | 22nd International Conference on Artificial Intelligence and Statistics - Naha, Japan Duration: 16 Apr 2019 → 18 Apr 2019 https://www.aistats.org/ |
Publication series
Name | Proceedings of Machine Learning Research |
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Publisher | PMLR |
Volume | 89 |
ISSN (Electronic) | 2640-3498 |
Conference
Conference | 22nd International Conference on Artificial Intelligence and Statistics |
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Abbreviated title | AISTATS 2019 |
Country/Territory | Japan |
City | Naha |
Period | 16/04/19 → 18/04/19 |
Internet address |
Fingerprint
Dive into the research topics of 'Multi-Task Time Series Analysis applied to Drug Response Modelling'. Together they form a unique fingerprint.Projects
- 2 Finished
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Improving decision support for trating arterial hypotension in adult patients during their management in intensive care
Williams, C. (Principal Investigator)
UK central government bodies/local authorities, health and hospital authorities
1/05/13 → 30/04/15
Project: Research