Prediction model of transfer rate of intensive care unit patients based on frequent pattern growth algorithm
- VernacularTitle:基于频繁模式增长算法的重症监护室转出率预测模型研究
- Author:
Hongyi WEN
1
;
Long TIAN
Author Information
- Keywords: intensive care unit; frequent pattern growth algorithm; transfer-out rate; prediction; association rules; short-term; area under the curve; medical resource allocation
- From: Journal of Clinical Medicine in Practice 2025;29(17):110-115
- CountryChina
- Language:Chinese
- Abstract: Objective To construct a prediction model for the transfer-out rate of patients in the intensive care unit(ICU)based on frequent pattern growth(FP-Growth)algorithm and evaluate its application value.Methods A total of 4,000 ICU patients were selected as study subjects and divid-ed into model construction group and validation group.Clinical data from both groups were collected and compared.Association rule analysis based on the FP-Growth algorithm was performed in the model construction group.A prediction model for the ICU patient transfer-out rate was established by calcu-lating effective strong association rules among the elements in the final scanned set of the model con-struction group.In internal validation,the model's consistency was evaluated using calibration curves and other metrics.In external validation,the area under the receiver operating characteristic(ROC)curve(AUC)for predicting the ICU patient transfer-out rate was compared between the model con-struction group and the validation group.Results In the model construction group,with correspond-ing clinical data available,the transfer-out rates within 7 days,＞7 to14 days,and＞14 to 21 days were 71％,40％and 18％,respectively.During internal validation,the calibration curve demonstra-ted satisfactory consistency between predicted and observed values.In external validation,the AUC values for the model's predictions of transfer-out rates at 7 days,＞7 to14 days,and＞14 to 21 days in the model construction group were 0.880,0.861 and 0.654,respectively.Conclusion The pre-diction model for the ICU patient transfer-out rate exhibits favorable short-term(within 14 days)pre-dictive performance,and its application holds certain reference value for optimizing the overall treat-ment efficacy in the ICU and the allocation of medical resources.
