eXplainable AI Allows Predicting Upper Limb Rehabilitation Outcomes in Sub-Acute Stroke Patients-Reference-Cited by-同舟云学术

eXplainable AI Allows Predicting Upper Limb Rehabilitation Outcomes in Sub-Acute Stroke Patients

Published:2023-01 Issue:1 Volume:27 Page:263-273
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Gandolfi Marialuisa¹^ORCID,Boscolo Galazzo Ilaria²^ORCID,Gasparin Pavan Rudy¹,Cruciani Federica²^ORCID,Vale Nicola¹^ORCID,Picelli Alessandro¹,Storti Silvia Francesca²^ORCID,Smania Nicola¹,Menegaz Gloria²^ORCID

Affiliation:

1. Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy

2. Department of Computer Science, University of Verona, Verona, Italy

Funder

fondazione CariVerona

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10006388/09940534.pdf?arnumber=9940534

Reference68 articles.

1. Predicting upper limb recovery after stroke: The place of early shoulder and hand movement

2. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

3. Biomarkers of stroke recovery: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable

4. 17. A Value for n-Person Games

5. Are indexes of arm recovery related to daily life autonomy in patients with stroke;smania;Eur J Phys Rehabil Med,2009

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