论文标题
使用多层感知器和多元线性回归技术，住院患者的低钠血症预测低钠血症
Forecasting Hyponatremia in hospitalized patients Using Multilayer Perceptron and Multivariate Linear Regression Techniques
论文作者
论文摘要
由于低钠血症而住院的患者百分比越来越高。低钠血症是人血清中钠电解质的缺乏。如果没有积极治疗和管理，这种缺陷可能会沉迷于不良影响，并与住院或死亡率更长有关。这项工作可根据多层感知器和多元线性回归算法来预测患者的未来级钠水平。这项工作分析了患者的年龄，有关其他疾病的信息，例如糖尿病，肺炎，肝脏疾病，恶性肿瘤，肺，败血症，SIADH和患者的钠水平。将提出的MLP算法的结果与基于MLR算法的结果进行了比较。 MLP预测结果比MLR算法产生23-72个更高的预测结果。因此，拟议的MLR算法比MLR结果预测患者未来钠范围的MLR结果降低了平均平方错误率的57.1。此外，提出的MLR算法产生了27-50个较高的预测精度率。
The percentage of patients hospitalized due to hyponatremia is getting higher. Hyponatremia is the deficiency of sodium electrolyte in the human serum. This deficiency might indulge adverse effects and also associated with longer hospital stay or mortality, if it wasnt actively treated and managed. This work predicts the futuristic sodium levels of patients based on their history of health problems using multilayer perceptron and multivariate linear regression algorithm. This work analyses the patients age, information about other disease such as diabetes, pneumonia, liver-disease, malignancy, pulmonary, sepsis, SIADH, and sodium level of the patient during admission to the hospital. The results of the proposed MLP algorithm is compared with MLR algorithm based results. The MLP prediction results generates 23-72 of higher prediction results than MLR algorithm. Thus, proposed MLR algorithm has produced 57.1 of reduced mean squared error rate than the MLR results on predicting future sodium ranges of patients. Further, proposed MLR algorithm produces 27-50 of higher prediction precision rate.
