Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis
The administration of thrombolysis usually reduces the risk of death and the consequences of stroke in the acute phase. However, having received thrombolysis administration is not a prognostic factor for neurorehabilitation outcome in the subacute phase of stroke. It is conceivably due to the comple...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/13/2/334 |
| _version_ | 1797622084339236864 |
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| author | Marco Iosa Stefano Paolucci Gabriella Antonucci Irene Ciancarelli Giovanni Morone |
| author_facet | Marco Iosa Stefano Paolucci Gabriella Antonucci Irene Ciancarelli Giovanni Morone |
| author_sort | Marco Iosa |
| collection | DOAJ |
| description | The administration of thrombolysis usually reduces the risk of death and the consequences of stroke in the acute phase. However, having received thrombolysis administration is not a prognostic factor for neurorehabilitation outcome in the subacute phase of stroke. It is conceivably due to the complex intertwining of many clinical factors. An artificial neural network (ANN) analysis could be helpful in identifying the prognostic factors of neurorehabilitation outcomes and assigning a weight to each of the factors considered. This study hypothesizes that the prognostic factors could be different between patients who received and those who did not receive thrombolytic treatment, even if thrombolysis is not a prognostic factor per se. In a sample of 862 patients with ischemic stroke, the tested ANN identified some common factors (such as disability at admission, age, unilateral spatial neglect), some factors with higher weight in patients who received thrombolysis (hypertension, epilepsy, aphasia, obesity), and some other factors with higher weight in the other patients (dysphagia, malnutrition, total arterial circulatory infarction). Despite the fact that thrombolysis is not an independent prognostic factor for neurorehabilitation, it seems to modify the relative importance of other clinical factors in predicting which patients will better respond to neurorehabilitation. |
| first_indexed | 2024-03-11T09:06:13Z |
| format | Article |
| id | doaj.art-b1a70e2fa26844e3b97410d837e84810 |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-11T09:06:13Z |
| publishDate | 2023-02-01 |
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| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-b1a70e2fa26844e3b97410d837e848102023-11-16T19:23:43ZengMDPI AGBiomolecules2218-273X2023-02-0113233410.3390/biom13020334Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with ThrombolysisMarco Iosa0Stefano Paolucci1Gabriella Antonucci2Irene Ciancarelli3Giovanni Morone4Department of Psychology, Sapienza University of Rome, 00185 Rome, ItalySmArt Lab, IRCCS Santa Lucia Foundation, 00179 Rome, ItalyDepartment of Psychology, Sapienza University of Rome, 00185 Rome, ItalyDepartment of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, ItalyDepartment of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, ItalyThe administration of thrombolysis usually reduces the risk of death and the consequences of stroke in the acute phase. However, having received thrombolysis administration is not a prognostic factor for neurorehabilitation outcome in the subacute phase of stroke. It is conceivably due to the complex intertwining of many clinical factors. An artificial neural network (ANN) analysis could be helpful in identifying the prognostic factors of neurorehabilitation outcomes and assigning a weight to each of the factors considered. This study hypothesizes that the prognostic factors could be different between patients who received and those who did not receive thrombolytic treatment, even if thrombolysis is not a prognostic factor per se. In a sample of 862 patients with ischemic stroke, the tested ANN identified some common factors (such as disability at admission, age, unilateral spatial neglect), some factors with higher weight in patients who received thrombolysis (hypertension, epilepsy, aphasia, obesity), and some other factors with higher weight in the other patients (dysphagia, malnutrition, total arterial circulatory infarction). Despite the fact that thrombolysis is not an independent prognostic factor for neurorehabilitation, it seems to modify the relative importance of other clinical factors in predicting which patients will better respond to neurorehabilitation.https://www.mdpi.com/2218-273X/13/2/334cerebrovascular accidentbraininjuryrehabilitationmachine learningartificial intelligence |
| spellingShingle | Marco Iosa Stefano Paolucci Gabriella Antonucci Irene Ciancarelli Giovanni Morone Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis Biomolecules cerebrovascular accident brain injury rehabilitation machine learning artificial intelligence |
| title | Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis |
| title_full | Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis |
| title_fullStr | Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis |
| title_full_unstemmed | Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis |
| title_short | Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis |
| title_sort | application of an artificial neural network to identify the factors influencing neurorehabilitation outcomes of patients with ischemic stroke treated with thrombolysis |
| topic | cerebrovascular accident brain injury rehabilitation machine learning artificial intelligence |
| url | https://www.mdpi.com/2218-273X/13/2/334 |
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