This talk discussed on an application of multi-state modelling to predict treatment pathways of a disease with heterogeneous disease management options, often involving multiple lines of active treatment.

Despite having notable advantages over established machine learning methods for time series analysis, reservoir computing methods, such as echo state networks (ESNs), have yet to be widely used for practical data mining applications. In this paper, we address this deficit with a case study that demonstrates how ESNs can be trained to predict disease labels when stimulated with movement data. Since there has been relatively little prior research into using ESNs for classification, we also consider a number of different approaches for realising input–output mappings. Our results show that ESNs can carry out effective classification and are competitive with existing approaches that have significantly longer training times, in addition to performing similarly with models employing conventional feature extraction strategies that require expert domain knowledge. This suggests that ESNs may prove beneficial in situations where predictive models must be trained rapidly and without the benefit of domain knowledge, for example on high-dimensional data produced by wearable medical technologies. This application area is emphasized with a case study of Parkinson’s disease patients who have been recorded by wearable sensors while performing basic movement tasks.

This work presented an interactive web application for building multi-state models of disease pathways. The app is flexible, allowing for both parametric and semi-parametric models, with transition-specific distributions. The presentation won the award for Best Presentation.

A survey of possible ways to evaluate survival models that are intended for prognostic, rather than inferential aims. The work was demonstrated on a clinically motivated data set of Follicular Lymphoma. This presentation won the Best in Session Award.

Ensembles are groups of classifiers which cooperate in order to reach a decision. Conventionally, the members of an ensemble are trained sequentially, and typically independently, and are not brought together until the final stages of ensemble generation. In this paper, we discuss the potential benefits of training classifiers together, so that they learn to interact at an early stage of their development. As a potential mechanism for achieving this, we consider the biological concept of mutualism, whereby cooperation emerges over the course of biological evolution. We also discuss potential mechanisms for implementing this approach within an evolutionary algorithm context