Machine Learning-Based Prediction Models of Acute Respiratory Failure in Patients with Acute Pesticide Poisoningopen access
- Authors
- Kim, Yeongmin; Chae, Minsu; Cho, Namjun; Gil, Hyowook; Lee, Hwamin
- Issue Date
- Dec-2022
- Publisher
- MDPI AG
- Keywords
- machine learning; respiratory failure; acute pesticide poisoning; logistic regression; random forests; long short-term memory
- Citation
- Mathematics, v.10, no.24
- Journal Title
- Mathematics
- Volume
- 10
- Number
- 24
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/22066
- DOI
- 10.3390/math10244633
- ISSN
- 2227-7390
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Medicine > Department of Internal Medicine > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.