Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-stroke Patients: A Scoping Review
Abstract Stroke is one of the leading causes of long-term disabilities in motor and cognition functionality. An early and accurate prediction of rehabilitation outcomes can lead to a tailor-made treatment that can significantly improve the post-stroke quality of life of a person. This scoping review...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2023-12-01
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| Series: | Human-Centric Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44230-023-00051-1 |
| _version_ | 1797199419245854720 |
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| author | Kyriakos Apostolidis Christos Kokkotis Serafeim Moustakidis Evangelos Karakasis Paraskevi Sakellari Christina Koutra Dimitrios Tsiptsios Stella Karatzetzou Konstantinos Vadikolias Nikolaos Aggelousis |
| author_facet | Kyriakos Apostolidis Christos Kokkotis Serafeim Moustakidis Evangelos Karakasis Paraskevi Sakellari Christina Koutra Dimitrios Tsiptsios Stella Karatzetzou Konstantinos Vadikolias Nikolaos Aggelousis |
| author_sort | Kyriakos Apostolidis |
| collection | DOAJ |
| description | Abstract Stroke is one of the leading causes of long-term disabilities in motor and cognition functionality. An early and accurate prediction of rehabilitation outcomes can lead to a tailor-made treatment that can significantly improve the post-stroke quality of life of a person. This scoping review aimed to summarize studies that use Artificial Intelligence (AI) for the prediction of language and cognition rehabilitation outcomes and the need to use AI in this domain. This study followed the PRISMA-ScR guidelines for two databases, Scopus and PubMed. The results, which are measured with several metrics depending on the task, regression, or classification, present encouraging outcomes as they can predict the cognitive functionality of post-stroke patients with relative precision. Among the results of the paper are the identification of the most effective Machine Learning (ML) algorithms, and the identification of the key factors that influence rehabilitation outcomes. The majority of studies focus on aphasia and present high performance achieving up to 97% recall and 91.4% precision. The main limitations of the studies were the small subject population and the lack of an external dataset. However, effective ML algorithms along with explainability are expected to become among the most prominent solutions for precision medicine due to their ability to overcome non-linearities on data and provide insights and transparent predictions that can help healthcare professionals make more informed and accurate decisions. |
| first_indexed | 2024-04-24T07:15:27Z |
| format | Article |
| id | doaj.art-b44737fb55e74a0f956d5cb8b5832f92 |
| institution | Directory Open Access Journal |
| issn | 2667-1336 |
| language | English |
| last_indexed | 2024-04-24T07:15:27Z |
| publishDate | 2023-12-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Human-Centric Intelligent Systems |
| spelling | doaj.art-b44737fb55e74a0f956d5cb8b5832f922024-04-21T11:21:15ZengSpringer NatureHuman-Centric Intelligent Systems2667-13362023-12-014114716010.1007/s44230-023-00051-1Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-stroke Patients: A Scoping ReviewKyriakos Apostolidis0Christos Kokkotis1Serafeim Moustakidis2Evangelos Karakasis3Paraskevi Sakellari4Christina Koutra5Dimitrios Tsiptsios6Stella Karatzetzou7Konstantinos Vadikolias8Nikolaos Aggelousis9Department of Physical Education and Sport Science, Democritus University of ThraceDepartment of Physical Education and Sport Science, Democritus University of ThraceDepartment of Physical Education and Sport Science, Democritus University of ThraceDepartment of Physical Education and Sport Science, Democritus University of ThraceDepartment of Physical Education and Sport Science, Democritus University of ThraceDepartment of Physical Education and Sport Science, Democritus University of ThraceDepartment of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of ThraceDepartment of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of ThraceDepartment of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of ThraceDepartment of Physical Education and Sport Science, Democritus University of ThraceAbstract Stroke is one of the leading causes of long-term disabilities in motor and cognition functionality. An early and accurate prediction of rehabilitation outcomes can lead to a tailor-made treatment that can significantly improve the post-stroke quality of life of a person. This scoping review aimed to summarize studies that use Artificial Intelligence (AI) for the prediction of language and cognition rehabilitation outcomes and the need to use AI in this domain. This study followed the PRISMA-ScR guidelines for two databases, Scopus and PubMed. The results, which are measured with several metrics depending on the task, regression, or classification, present encouraging outcomes as they can predict the cognitive functionality of post-stroke patients with relative precision. Among the results of the paper are the identification of the most effective Machine Learning (ML) algorithms, and the identification of the key factors that influence rehabilitation outcomes. The majority of studies focus on aphasia and present high performance achieving up to 97% recall and 91.4% precision. The main limitations of the studies were the small subject population and the lack of an external dataset. However, effective ML algorithms along with explainability are expected to become among the most prominent solutions for precision medicine due to their ability to overcome non-linearities on data and provide insights and transparent predictions that can help healthcare professionals make more informed and accurate decisions.https://doi.org/10.1007/s44230-023-00051-1StrokeAphasiaCognitiveArtificial intelligencePrognosis |
| spellingShingle | Kyriakos Apostolidis Christos Kokkotis Serafeim Moustakidis Evangelos Karakasis Paraskevi Sakellari Christina Koutra Dimitrios Tsiptsios Stella Karatzetzou Konstantinos Vadikolias Nikolaos Aggelousis Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-stroke Patients: A Scoping Review Human-Centric Intelligent Systems Stroke Aphasia Cognitive Artificial intelligence Prognosis |
| title | Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-stroke Patients: A Scoping Review |
| title_full | Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-stroke Patients: A Scoping Review |
| title_fullStr | Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-stroke Patients: A Scoping Review |
| title_full_unstemmed | Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-stroke Patients: A Scoping Review |
| title_short | Machine Learning Algorithms for the Prediction of Language and Cognition Rehabilitation Outcomes of Post-stroke Patients: A Scoping Review |
| title_sort | machine learning algorithms for the prediction of language and cognition rehabilitation outcomes of post stroke patients a scoping review |
| topic | Stroke Aphasia Cognitive Artificial intelligence Prognosis |
| url | https://doi.org/10.1007/s44230-023-00051-1 |
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