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Multi-Task Time Series Analysis applied to Drug Response Modelling

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http://proceedings.mlr.press/v89/bird19a.html
Original languageEnglish
Title of host publicationProceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics
EditorsNeil Lawrence, Mark Reid
PublisherPMLR
Number of pages10
Volume89
Publication statusE-pub ahead of print - 18 Apr 2019
Event22nd International Conference on Artificial Intelligence and Statistics - Naha, Japan
Duration: 16 Apr 201918 Apr 2019
https://www.aistats.org/

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume89
ISSN (Electronic)2640-3498

Conference

Conference22nd International Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS 2019
CountryJapan
CityNaha
Period16/04/1918/04/19
Internet address

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.

Event

22nd International Conference on Artificial Intelligence and Statistics

16/04/1918/04/19

Naha, Japan

Event: Conference

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