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From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning

As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disabilit...

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Autores principales: Gkantzios, Aimilios, Kokkotis, Christos, Tsiptsios, Dimitrios, Moustakidis, Serafeim, Gkartzonika, Elena, Avramidis, Theodoros, Tripsianis, Gregory, Iliopoulos, Ioannis, Aggelousis, Nikolaos, Vadikolias, Konstantinos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10532952/
https://www.ncbi.nlm.nih.gov/pubmed/37763143
http://dx.doi.org/10.3390/jpm13091375
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author Gkantzios, Aimilios
Kokkotis, Christos
Tsiptsios, Dimitrios
Moustakidis, Serafeim
Gkartzonika, Elena
Avramidis, Theodoros
Tripsianis, Gregory
Iliopoulos, Ioannis
Aggelousis, Nikolaos
Vadikolias, Konstantinos
author_facet Gkantzios, Aimilios
Kokkotis, Christos
Tsiptsios, Dimitrios
Moustakidis, Serafeim
Gkartzonika, Elena
Avramidis, Theodoros
Tripsianis, Gregory
Iliopoulos, Ioannis
Aggelousis, Nikolaos
Vadikolias, Konstantinos
author_sort Gkantzios, Aimilios
collection PubMed
description As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FN(rate) and 4.59% FP(rate). Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients’ NIHSS progression from the time of admission until their discharge.
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spelling pubmed-105329522023-09-28 From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning Gkantzios, Aimilios Kokkotis, Christos Tsiptsios, Dimitrios Moustakidis, Serafeim Gkartzonika, Elena Avramidis, Theodoros Tripsianis, Gregory Iliopoulos, Ioannis Aggelousis, Nikolaos Vadikolias, Konstantinos J Pers Med Article As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FN(rate) and 4.59% FP(rate). Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients’ NIHSS progression from the time of admission until their discharge. MDPI 2023-09-14 /pmc/articles/PMC10532952/ /pubmed/37763143 http://dx.doi.org/10.3390/jpm13091375 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gkantzios, Aimilios
Kokkotis, Christos
Tsiptsios, Dimitrios
Moustakidis, Serafeim
Gkartzonika, Elena
Avramidis, Theodoros
Tripsianis, Gregory
Iliopoulos, Ioannis
Aggelousis, Nikolaos
Vadikolias, Konstantinos
From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning
title From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning
title_full From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning
title_fullStr From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning
title_full_unstemmed From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning
title_short From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning
title_sort from admission to discharge: predicting national institutes of health stroke scale progression in stroke patients using biomarkers and explainable machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10532952/
https://www.ncbi.nlm.nih.gov/pubmed/37763143
http://dx.doi.org/10.3390/jpm13091375
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