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Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms. Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ (MIMIC-Ⅳ) database from 2008 to 2019. According to the occurrence of posttraumatic fatal massive hemorrhage, the patients were divided into two groups: fatal massive hemorrhage group (n=283) and non-fatal massive hemorrhage group (n=7239). Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forests (RF), adaptive boosting (AdaBoost), gated recurrent unit (GRU), and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma. The probability of fatal massive hemorrhage in the following 1, 2, and 3 h was dynamically predicted. The performance of the models was evaluated by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, Youden index, and area under receiver operating characteristic curve (AUC). The models were externally validated based on the trauma database of the Chinese PLA General Hospital. Results In the MIMIC-Ⅳ database, the set of dynamic prediction models based on the GRU-D algorithm was the best. The AUC for predicting fatal major bleeding in the next 1, 2, and 3 h were 0.946±0.029, 0.940±0.032, and 0.943±0.034, respectively, and there was no significant difference (P=0.905). In the trauma dataset, GRU-D model achieved the best external validation effect. The AUC for predicting fatal major bleeding in the next 1, 2, and 3 h were 0.779±0.013, 0.780±0.008, and 0.778±0.009, respectively, and there was no significant difference (P=0.181). This set of models was deployed in a public web calculator and hospital emergency department information system, which is convenient for the public and medical staff to use and validate the model. Conclusion A set of dynamic prediction models has been successfully developed and validated, which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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