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Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage

Background: A predictive model can provide physicians, relatives, and patients the accurate information regarding the severity of disease and its predicted outcome. In this study, we used an automated machine-learning-based approach to construct a prognostic model to predict the functional outcome i...

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Autores principales: Wang, Hsueh-Lin, Hsu, Wei-Yen, Lee, Ming-Hsueh, Weng, Hsu-Huei, Chang, Sheng-Wei, Yang, Jen-Tsung, Tsai, Yuan-Hsiung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713018/
https://www.ncbi.nlm.nih.gov/pubmed/31496988
http://dx.doi.org/10.3389/fneur.2019.00910
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author Wang, Hsueh-Lin
Hsu, Wei-Yen
Lee, Ming-Hsueh
Weng, Hsu-Huei
Chang, Sheng-Wei
Yang, Jen-Tsung
Tsai, Yuan-Hsiung
author_facet Wang, Hsueh-Lin
Hsu, Wei-Yen
Lee, Ming-Hsueh
Weng, Hsu-Huei
Chang, Sheng-Wei
Yang, Jen-Tsung
Tsai, Yuan-Hsiung
author_sort Wang, Hsueh-Lin
collection PubMed
description Background: A predictive model can provide physicians, relatives, and patients the accurate information regarding the severity of disease and its predicted outcome. In this study, we used an automated machine-learning-based approach to construct a prognostic model to predict the functional outcome in patients with primary intracerebral hemorrhage (ICH). Methods: We retrospectively collected data on demographic characteristics, laboratory studies and imaging findings of 333 patients with primary ICH. The functional outcomes at the 1st and 6th months after ICH were defined by the modified Rankin scale. All of the attributes were used for preprocessing and for automatic model selection with Automatic Waikato Environment for Knowledge Analysis. Confusion matrix and areas under the receiver operating characteristic curves (AUC) were used to test the predictive performance. Results: Among the models tested, the random forest provided the best predictive performance for functional outcome. The overall accuracy for predicting the 1st month outcome was 83.1%, with 77.4% sensitivity and 86.9% specificity, and the AUC was 0.899. The overall accuracy for predicting the 6th month outcome was 83.9%, with 72.5% sensitivity and 90.6% specificity, and the AUC was 0.917. Conclusions: Using an automatic machine learning technique to predict functional outcome after ICH is feasible, and the random forest model provides the best predictive performance across all tested models. This prediction model may provide information regarding functional outcome for clinicians that will help provide appropriate medical care for patients and information for their caregivers.
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spelling pubmed-67130182019-09-06 Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage Wang, Hsueh-Lin Hsu, Wei-Yen Lee, Ming-Hsueh Weng, Hsu-Huei Chang, Sheng-Wei Yang, Jen-Tsung Tsai, Yuan-Hsiung Front Neurol Neurology Background: A predictive model can provide physicians, relatives, and patients the accurate information regarding the severity of disease and its predicted outcome. In this study, we used an automated machine-learning-based approach to construct a prognostic model to predict the functional outcome in patients with primary intracerebral hemorrhage (ICH). Methods: We retrospectively collected data on demographic characteristics, laboratory studies and imaging findings of 333 patients with primary ICH. The functional outcomes at the 1st and 6th months after ICH were defined by the modified Rankin scale. All of the attributes were used for preprocessing and for automatic model selection with Automatic Waikato Environment for Knowledge Analysis. Confusion matrix and areas under the receiver operating characteristic curves (AUC) were used to test the predictive performance. Results: Among the models tested, the random forest provided the best predictive performance for functional outcome. The overall accuracy for predicting the 1st month outcome was 83.1%, with 77.4% sensitivity and 86.9% specificity, and the AUC was 0.899. The overall accuracy for predicting the 6th month outcome was 83.9%, with 72.5% sensitivity and 90.6% specificity, and the AUC was 0.917. Conclusions: Using an automatic machine learning technique to predict functional outcome after ICH is feasible, and the random forest model provides the best predictive performance across all tested models. This prediction model may provide information regarding functional outcome for clinicians that will help provide appropriate medical care for patients and information for their caregivers. Frontiers Media S.A. 2019-08-21 /pmc/articles/PMC6713018/ /pubmed/31496988 http://dx.doi.org/10.3389/fneur.2019.00910 Text en Copyright © 2019 Wang, Hsu, Lee, Weng, Chang, Yang and Tsai. http://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
Wang, Hsueh-Lin
Hsu, Wei-Yen
Lee, Ming-Hsueh
Weng, Hsu-Huei
Chang, Sheng-Wei
Yang, Jen-Tsung
Tsai, Yuan-Hsiung
Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage
title Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage
title_full Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage
title_fullStr Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage
title_full_unstemmed Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage
title_short Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage
title_sort automatic machine-learning-based outcome prediction in patients with primary intracerebral hemorrhage
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713018/
https://www.ncbi.nlm.nih.gov/pubmed/31496988
http://dx.doi.org/10.3389/fneur.2019.00910
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