Multi-task learning for predicting quality-of-life and independence in activities of daily living after stroke : a proof-of-concept study
Nguyen, Thi Nguyet Que (Technological University Dublin)
Garcia-Rudolph, Alejandro (Universitat Autònoma de Barcelona. Departament de Medicina)
Saurí, Joan (Universitat Autònoma de Barcelona. Departament de Medicina)
Kelleher, John D. (Trinity College Dublin)

Data:	2024
Resum:	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.
Ajuts:	European Commission 777107
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Multi-task learning ; Task grouping ; Stroke ; Activities of daily living ; Quality-of-life ; Barthel index ; EQ-5D-3L
Publicat a:	Frontiers in neurology, Vol. 15 (september 2024) , ISSN 1664-2295

DOI: 10.3389/fneur.2024.1449234
PMID: 39399874

26 p, 2.4 MB

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