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Predicting the Risk of Death for Sepsis Based on Within-Class Mixup and Lightgbm

Published: 25 February 2022 Publication History

Abstract

Sepsis is a disease with a high mortality rate in intensive care units. Rapid and accurate identification of the hospitalization risk of sepsis patients is helpful for doctors to intervene in time. To address the problem of complexity, diversity, and imbalance of medical data in hospital, the prediction method, named Mu-Lightgbm, is proposed. Selectively referring to the disease severity scoring system, a public critical care medicine database (MIMIC-III) is used, and demographic information and laboratorial examination data are used as characteristic variables. Firstly, according to the distribution and category of the sample data, the within-class Mixup method is used to augment the sample data to ensure the balance between each class. Secondly, the Lightgbm model for risk prediction is constructed and trained with the processed data. A total of 23741 sepsis patients are collected from the MIMIC-III database with a mortality rate of 19.03%. Five-fold cross-validation shows that the AUC of Mu-Lightgbm model and Lightgbm model are 0.93 and 0.91, respectively. Compared with the existing prediction models, the proposed model performs better in prediction accuracy, which can assist clinicians in more timely treatment.

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          cover image ACM Other conferences
          AIPR '21: Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
          September 2021
          715 pages
          ISBN:9781450384087
          DOI:10.1145/3488933
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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          Published: 25 February 2022

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          Author Tags

          1. Intensive care unit
          2. Lightgbm
          3. MIMIC-III database
          4. Mixup
          5. Sepsis

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