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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1449234
This article is part of the Research Topic AI in Neuro-intervention and Neurological Care View all articles

Multi-task learning for predicting quality-of-life and independence in activities of daily living after stroke: a proof-of-concept study

Provisionally accepted
Thi Nguyet Que Nguyen Thi Nguyet Que Nguyen 1*Alejandro García-Rudolph Alejandro García-Rudolph 2,3,4Joan Saurí Joan Saurí 2,3,4John D. Kelleher John D. Kelleher 5
  • 1 Technological University Dublin, Dublin, Ireland
  • 2 Department of Research and Innovation, University Institute for Neurorehabilitation, Guttmann Institute, Barcelona, Catalonia, Spain
  • 3 Departament de Medicina, Universitat Autonoma De Barcelona, Bellaterra, Spain
  • 4 Fundacio Institut d'Investigacio En Ciencies De La Salut Germans Trias I Pujol, Barcelona, Spain
  • 5 Adapt Centre, School of Computer Science and Statistics, Faculty of Engineering, Mathematics and Science, Trinity College Dublin, Dublin, Ireland

The final, formatted version of the article will be published soon.

    A health-related (HR) profile is a set of multiple health-related items recording the status of the patient at different follow-up times post-stroke. In order to support clinicians in designing rehabilitation treatment programs, we propose a novel multi-task learning (MTL) strategy for predicting post-stroke patient HR profiles. The HR profile in this study is measured by the Barthel index (BI) assessment or by the EQ-5D-3L questionnaire. Three datasets are used in this work and for each dataset six neural network architectures are developed and tested. Results indicate that an MTL architecture combining a pre-trained network for all tasks with a concatenation strategy conditioned by a task grouping method is a promising approach for predicting the HR profile of a patient with stroke at different phases of the patient journey.These models obtained a mean F1-score of 0.434 (standard deviation 0.022, confidence interval at 95% [0.428, 0.44]) calculated across all the items when predicting BI at 3 months after stroke (MaS), 0.388 (standard deviation 0.029, confidence interval at 95% [0.38, 0.397]) when predicting EQ-5D-3L at 6MaS, and 0.462 (standard deviation 0.029, confidence interval at 95% [0.454, 0.47]) when predicting the EQ-5D-3L at 18MaS. Furthermore, our MTL architecture outperforms the reference single-task learning models and the classic MTL of all tasks in 8 out of 10 tasks when predicting BI at 3MaS and has better prediction performance than the reference models on all tasks when predicting EQ-5D-3L at 6 and 18MaS.The models we present in this paper are the first models to predict the components of the BI or the EQ-5D-3L, and our results demonstrate the potential benefits of using MTL in a health context to predict patient profiles.

    Keywords: Multi-task learning, Task grouping, Stroke, Activities of Daily Living, quality-of-life, barthel index, EQ-5D-3L

    Received: 14 Jun 2024; Accepted: 28 Aug 2024.

    Copyright: © 2024 Nguyen, García-Rudolph, Saurí and Kelleher. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Thi Nguyet Que Nguyen, Technological University Dublin, Dublin, Ireland

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.