Disease prediction method based on electronic medical record data
To further enhance the accuracy of clinical diagnosis and improve the precision of disease prediction.Therefore,a prediction method for gestational diabetes based on electronic medical record data is proposed.Firstly,the correctness of the learning algorithm is verified by evaluating the baseline accuracy of the samples before modelling,and the samples are balanced on this basis.Excessive bias in the model prediction results due to unbalanced data categories is eliminated.In this experiment,mean square error is used to validate the accuracy of the method.A logistic regression model was constructed from the training set and data from the test set was introduced into the prediction model.the F1 and AUC values for the logistic regression were 0.809,0.881 and 0.825 respectively,an increase of approximately 12%compared to when the feature was not used.The results suggest that electronic medical record data-driven can be effective in improving the accuracy of gestational diabetes prediction.
electronic medical recorddata Disease predictionClinical diagnosis
万方数据
维普
