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Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine
BACKGROUND: Recognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-tim...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203613/ https://www.ncbi.nlm.nih.gov/pubmed/35719136 http://dx.doi.org/10.1007/s13167-022-00283-4 |
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author | Zheng, Yulu Guo, Zheng Zhang, Yanbo Shang, Jianjing Yu, Leilei Fu, Ping Liu, Yizhi Li, Xingang Wang, Hao Ren, Ling Zhang, Wei Hou, Haifeng Tan, Xuerui Wang, Wei |
author_facet | Zheng, Yulu Guo, Zheng Zhang, Yanbo Shang, Jianjing Yu, Leilei Fu, Ping Liu, Yizhi Li, Xingang Wang, Hao Ren, Ling Zhang, Wei Hou, Haifeng Tan, Xuerui Wang, Wei |
author_sort | Zheng, Yulu |
collection | PubMed |
description | BACKGROUND: Recognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing. METHODS: This investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques—permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)—were applied for explaining the black-box ML models. RESULTS: Fifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90–0.92) and 0.92 (0.91–0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model’s prediction. LIME and SHAP showed similar local feature attribution explanations. CONCLUSION: In the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-022-00283-4. |
format | Online Article Text |
id | pubmed-9203613 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-92036132022-06-18 Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine Zheng, Yulu Guo, Zheng Zhang, Yanbo Shang, Jianjing Yu, Leilei Fu, Ping Liu, Yizhi Li, Xingang Wang, Hao Ren, Ling Zhang, Wei Hou, Haifeng Tan, Xuerui Wang, Wei EPMA J Research BACKGROUND: Recognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing. METHODS: This investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques—permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)—were applied for explaining the black-box ML models. RESULTS: Fifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90–0.92) and 0.92 (0.91–0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model’s prediction. LIME and SHAP showed similar local feature attribution explanations. CONCLUSION: In the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-022-00283-4. Springer International Publishing 2022-05-27 /pmc/articles/PMC9203613/ /pubmed/35719136 http://dx.doi.org/10.1007/s13167-022-00283-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Zheng, Yulu Guo, Zheng Zhang, Yanbo Shang, Jianjing Yu, Leilei Fu, Ping Liu, Yizhi Li, Xingang Wang, Hao Ren, Ling Zhang, Wei Hou, Haifeng Tan, Xuerui Wang, Wei Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine |
title | Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine |
title_full | Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine |
title_fullStr | Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine |
title_full_unstemmed | Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine |
title_short | Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine |
title_sort | rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203613/ https://www.ncbi.nlm.nih.gov/pubmed/35719136 http://dx.doi.org/10.1007/s13167-022-00283-4 |
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