Machine Learning-Based Prediction Models of Acute Respiratory Failure in Patients with Acute Pesticide Poisoning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Yeongmin | - |
dc.contributor.author | Chae, Minsu | - |
dc.contributor.author | Cho, Namjun | - |
dc.contributor.author | Gil, Hyowook | - |
dc.contributor.author | Lee, Hwamin | - |
dc.date.accessioned | 2023-03-09T02:41:41Z | - |
dc.date.available | 2023-03-09T02:41:41Z | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 2227-7390 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/22066 | - |
dc.description.abstract | The prognosis of patients with acute pesticide poisoning depends on their acute respiratory condition. Here, we propose machine learning models to predict acute respiratory failure in patients with acute pesticide poisoning using a decision tree, logistic regression, and random forests, support vector machine, adaptive boosting, gradient boosting, multi-layer boosting, recurrent neural network, long short-term memory, and gated recurrent gate. We collected medical records of patients with acute pesticide poisoning at the Soonchunhyang University Cheonan Hospital from 1 January 2016 to 31 December 2020. We applied the k-Nearest Neighbor Imputer algorithm, MissForest Impuer and average imputation method to handle the problems of missing values and outliers in electronic medical records. In addition, we used the min-max scaling method for feature scaling. Using the most recent medical research, p-values, tree-based feature selection, and recursive feature reduction, we selected 17 out of 81 features. We applied a sliding window of 3 h to every patient's medical record within 24 h. As the prevalence of acute respiratory failure in our dataset was 8%, we employed oversampling. We assessed the performance of our models in predicting acute respiratory failure. The proposed long short-term memory demonstrated a positive predictive value of 98.42%, a sensitivity of 97.91%, and an F1 score of 0.9816. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI AG | - |
dc.title | Machine Learning-Based Prediction Models of Acute Respiratory Failure in Patients with Acute Pesticide Poisoning | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/math10244633 | - |
dc.identifier.scopusid | 2-s2.0-85144665168 | - |
dc.identifier.wosid | 000904201000001 | - |
dc.identifier.bibliographicCitation | Mathematics, v.10, no.24 | - |
dc.citation.title | Mathematics | - |
dc.citation.volume | 10 | - |
dc.citation.number | 24 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics | - |
dc.subject.keywordPlus | SMOTE | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | respiratory failure | - |
dc.subject.keywordAuthor | acute pesticide poisoning | - |
dc.subject.keywordAuthor | logistic regression | - |
dc.subject.keywordAuthor | random forests | - |
dc.subject.keywordAuthor | long short-term memory | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.