| 摘要： | Background： Serum albumin level is a crucial nutritional indicator for patients on dialysis. Approximately onethird of patients on hemodialysis (HD) have protein malnutrition. Therefore, the serum albumin level of patients on HD is strongly correlated with mortality.
Methods： In study, the data sets were obtained from the longitudinal electronic health records of the largest HD center in Taiwan from July 2011 to December 2015, included 1,567 new patients on HD who met the inclusion criteria. Multivariate logistic regression was performed to evaluate the association of clinical factors with low serum albu min, and the grasshopper optimization algorithm (GOA) was used for feature selection. The quantile gcomputation method was used to calculate the weight ratio of each factor. Machine learning and deep learning (DL) methods were used to predict the low serum albumin. The area under the curve (AUC) and accuracy were calculated to determine the model performance.
Results： Age, gender, hypertension, hemoglobin, iron, ferritin, sodium, potassium, calcium, creatinine, alkaline phos phatase, and triglyceride levels were signifcantly associated with low serum albumin. The AUC and accuracy of the GOA quantile gcomputation weight model combined with the BiLSTM method were 98% and 95%, respectively.
Conclusion： The GOA method was able to rapidly identify the optimal combination of factors associated with serum albumin in patients on HD, and the quantile gcomputation with DL methods could determine the most efective GOA quantile gcomputation weight prediction model. The serum albumin status of patients on HD can be predicted by the proposed model and accordingly provide patients with better a prognostic care and treatment. |
