# Multi-Task Time Series Analysis applied to Drug Response Modelling

arXiv: https://arxiv.org/abs/1903.08970 · HTML: https://mlsanity.com/paper/1903.08970/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control](https://mlsanity.com/paper/2303.10180/) (2023)
- [Multi-Task Dynamical Systems](https://mlsanity.com/paper/2210.04023/) (2022)

## Abstract (arXiv)

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.