Multi-Task Time Series Analysis applied to Drug Response Modelling

2019/03/21 by Alexander Bird, Bird, Alex, Christopher K. I. Williams +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Medicine · Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Heart Rate Variability and Autonomic Control #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Receptor Mechanisms and Signaling

paper · pdf · doi:10.48550/arxiv.1903.08970

openalex publication_date 2019/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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