AEP-DLA: Adverse Event Prediction in Hospitalized Adult Patients Using Deep Learning Algorithms
| Autor: | Ying-Chih Lo, Ruey-Kai Sheu, Lun-Chi Chen, Ming-Ju Wu, Win-Tsung Lo, Mayuresh Sunil Pardeshi, Hsiu-Hui Yu, Chien-Chung Huang, Chieh-Liang Wu |
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| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science risk stratification 030204 cardiovascular system & hematology Convolutional neural network early deterioration indication 03 medical and health sciences 0302 clinical medicine General Materials Science 030212 general & internal medicine electronic medical record business.industry early warning scores Deep learning General Engineering Glasgow Coma Scale Early warning score Random forest Adverse event (AE) Recurrent neural network Every Hour lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence Data pre-processing business lcsh:TK1-9971 Algorithm |
| Zdroj: | IEEE Access, Vol 9, Pp 55673-55689 (2021) |
| ISSN: | 2169-3536 |
| Popis: | Early prediction of clinical deterioration such as adverse events (AEs), improves patient safety. National Early Warning Score (NEWS) is widely used to predict AEs based on the aggregation of 6 physiological parameters. We took the same parameters as the features for AE prediction using deep learning algorithms (AEP-DLA) among hospitalized adult patients. The aim of this study is to get better performance than traditional naïve mathematical calculations by introducing novel vital sign data preprocessing schemes. We retrospectively collected the data from our electronic medical record data warehouse (2007 ~ 2017). AE rate of all 99,861 admissions was 6.2%. The dataset was divided into training and testing datasets from 2007–2015 and 2016–2017 respectively. In real-life clinical care, physiological parameters were not recorded every hour and missed frequently, for example, Glasgow Coma Scale (GCS). The expert domain suggested that missed GCS was rated as 15. We took two strategies (stack series records and align by hour) in the data preprocessing and tripling the values of negative samples for class balancing (CB). We used the last 28 hours’ serial data to predict AEs 3 hours later with Random Forest, XGBoost, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). It is shown that CNN with CB and align by hour got the best results comparing to the other methods. The precision, recall and area under curve were 0.841, 0.928 and 0.995 respectively. The performance of the model is also better than those proposed in the published literatures. |
| Databáze: | OpenAIRE |
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