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- W4382052878 abstract "Wound infection is a common clinical symptom, which not only brings economic and psychological burden to patients, but also increases the difficulty of wound treatment. It is feasible to predict the bacterial infection of wounds based on the current rapidly developing machine learning technologies and wound microenvironment indicator data. However, due to the confidentiality of clinical data, the complexity of medical experiments and other reasons, it is usually impossible to obtain a large amount of standard clinical data. Therefore, based on a small amount of wound micro-environment data collected in relevant experiments of Tangdu Hospital of Air Force Military Medical University, we propose a multivariate time series data augmentation method based on DTW to improve the performance of machine learning methods on the task of wound infection prediction. Through experiments, we verified the effectiveness of the method, and the prediction accuracy of NN-DTW was improved by 20.61%. Furthermore, we also conducted experimental verification on some datasets of the UCR/UEA archives, and the results show that the method is also effective for those time series datasets." @default.
- W4382052878 created "2023-06-27" @default.
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- W4382052878 date "2023-04-26" @default.
- W4382052878 modified "2023-09-26" @default.
- W4382052878 title "A Data Augmentation Method for Wound Infection Prediction" @default.
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- W4382052878 doi "https://doi.org/10.1109/icccbda56900.2023.10154745" @default.
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