A Time-Critical Topic Model for Predicting the Survival Time of Sepsis Patients
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions of dollars annually worldwide. Predicting survival time for sepsis patients is a time-critical prediction problem. Considering the useful sequential information for sepsis development, this paper proposes a t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2020-01-01
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| Series: | Scientific Programming |
| Online Access: | http://dx.doi.org/10.1155/2020/8884539 |
| Summary: | Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions of dollars annually worldwide. Predicting survival time for sepsis patients is a time-critical prediction problem. Considering the useful sequential information for sepsis development, this paper proposes a time-critical topic model (TiCTM) inspired by the latent Dirichlet allocation (LDA) model. The proposed TiCTM approach takes into account the time dependency structure between notes, measurement, and survival time of a sepsis patient. Experimental results on the public MIMIC-III database show that, overall, our method outperforms the conventional LDA and linear regression model in terms of recall, precision, accuracy, and F1-measure. It is also found that our method achieves the best performance by using 5 topics when predicting the probability for 30-day survival time. |
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| ISSN: | 1058-9244 1875-919X |