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Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke
BACKGROUND: Accurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of func...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9990416/ https://www.ncbi.nlm.nih.gov/pubmed/36895902 http://dx.doi.org/10.3389/fneur.2023.1114360 |
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author | Fast, Lea Temuulen, Uchralt Villringer, Kersten Kufner, Anna Ali, Huma Fatima Siebert, Eberhard Huo, Shufan Piper, Sophie K. Sperber, Pia Sophie Liman, Thomas Endres, Matthias Ritter, Kerstin |
author_facet | Fast, Lea Temuulen, Uchralt Villringer, Kersten Kufner, Anna Ali, Huma Fatima Siebert, Eberhard Huo, Shufan Piper, Sophie K. Sperber, Pia Sophie Liman, Thomas Endres, Matthias Ritter, Kerstin |
author_sort | Fast, Lea |
collection | PubMed |
description | BACKGROUND: Accurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of functional recovery, cognitive function, depression, and mortality of first-ever ischemic stroke patients and to identify the leading prognostic factors. METHODS: We predicted clinical outcomes for 307 patients (151 females, 156 males; 68 ± 14 years) from the PROSpective Cohort with Incident Stroke Berlin study using 43 baseline features. Outcomes included modified Rankin Scale (mRS), Barthel Index (BI), Mini-Mental State Examination (MMSE), Modified Telephone Interview for Cognitive Status (TICS-M), Center for Epidemiologic Studies Depression Scale (CES-D) and survival. The ML models included a Support Vector Machine with a linear kernel and a radial basis function kernel as well as a Gradient Boosting Classifier based on repeated 5-fold nested cross-validation. The leading prognostic features were identified using Shapley additive explanations. RESULTS: The ML models achieved significant prediction performance for mRS at patient discharge and after 1 year, BI and MMSE at patient discharge, TICS-M after 1 and 3 years and CES-D after 1 year. Additionally, we showed that National Institutes of Health Stroke Scale (NIHSS) was the top predictor for most functional recovery outcomes as well as education for cognitive function and depression. CONCLUSION: Our machine learning analysis successfully demonstrated the ability to predict clinical outcomes after first-ever ischemic stroke and identified the leading prognostic factors that contribute to this prediction. |
format | Online Article Text |
id | pubmed-9990416 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99904162023-03-08 Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke Fast, Lea Temuulen, Uchralt Villringer, Kersten Kufner, Anna Ali, Huma Fatima Siebert, Eberhard Huo, Shufan Piper, Sophie K. Sperber, Pia Sophie Liman, Thomas Endres, Matthias Ritter, Kerstin Front Neurol Neurology BACKGROUND: Accurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of functional recovery, cognitive function, depression, and mortality of first-ever ischemic stroke patients and to identify the leading prognostic factors. METHODS: We predicted clinical outcomes for 307 patients (151 females, 156 males; 68 ± 14 years) from the PROSpective Cohort with Incident Stroke Berlin study using 43 baseline features. Outcomes included modified Rankin Scale (mRS), Barthel Index (BI), Mini-Mental State Examination (MMSE), Modified Telephone Interview for Cognitive Status (TICS-M), Center for Epidemiologic Studies Depression Scale (CES-D) and survival. The ML models included a Support Vector Machine with a linear kernel and a radial basis function kernel as well as a Gradient Boosting Classifier based on repeated 5-fold nested cross-validation. The leading prognostic features were identified using Shapley additive explanations. RESULTS: The ML models achieved significant prediction performance for mRS at patient discharge and after 1 year, BI and MMSE at patient discharge, TICS-M after 1 and 3 years and CES-D after 1 year. Additionally, we showed that National Institutes of Health Stroke Scale (NIHSS) was the top predictor for most functional recovery outcomes as well as education for cognitive function and depression. CONCLUSION: Our machine learning analysis successfully demonstrated the ability to predict clinical outcomes after first-ever ischemic stroke and identified the leading prognostic factors that contribute to this prediction. Frontiers Media S.A. 2023-02-21 /pmc/articles/PMC9990416/ /pubmed/36895902 http://dx.doi.org/10.3389/fneur.2023.1114360 Text en Copyright © 2023 Fast, Temuulen, Villringer, Kufner, Ali, Siebert, Huo, Piper, Sperber, Liman, Endres and Ritter. https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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. |
spellingShingle | Neurology Fast, Lea Temuulen, Uchralt Villringer, Kersten Kufner, Anna Ali, Huma Fatima Siebert, Eberhard Huo, Shufan Piper, Sophie K. Sperber, Pia Sophie Liman, Thomas Endres, Matthias Ritter, Kerstin Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke |
title | Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke |
title_full | Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke |
title_fullStr | Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke |
title_full_unstemmed | Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke |
title_short | Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke |
title_sort | machine learning-based prediction of clinical outcomes after first-ever ischemic stroke |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9990416/ https://www.ncbi.nlm.nih.gov/pubmed/36895902 http://dx.doi.org/10.3389/fneur.2023.1114360 |
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