Using Machine Learning Algorithms in Medication for Cardiac Arrest Early Warning System Construction and Forecasting

Abstract

Target-In this paper, we focus on using medicine for patients who have cardiac arrest then must have to do Cardiopulmonary Resuscitation (CPR). We want to know the medicine influence in predicting state of an illness deterioration. Therefore, we proposes a Medication for Cardiac Arrest Early Warning System (MCAEWS). It's not only assist physicians to early diagnose of an illness and immediately warning, but also increase sensitivity, decrease false positive rate and mortality rate. The most important role is greatly improve medical quality. Methods-In this study, the data is from the emergency department of National Taiwan University Hospital (NTUH). It is from January 2014 to December 2015. The patients who stayed in the emergency detention area for more than six hours during this two years. The patients were included in the retrospective cohort study. To comparative measures for the machine learning models, we used such as the Area Under the Receiver Operating Characteristic Curve (AUROC) and the Area under the Precision-Recall Curve (AUPRC). Results-The data were analyzed for CPR and non-CPR groups respectively. Furthermore, we evaluated sensitivity and specificity. The Random Forest Algorithm (AUC: 0.98; AUP: 0.23) compare with others such as Logistic Regression Algorithm (AUC: 0.94; AUP: 0.13), Decision Tree (AUC: 0.97; AUP: 0.05), and Extreme Random Tree (AUC: 0.91; AUP: 0.08), it was significantly high performance. Conclusion-Increasing the drug factors in vital signs, that it effectively improved the accuracy of predicting cardiac arrest. The results of this study, it's help for emergency clinical Physicians and hospital quality management will validly solve clinical medical resource allocation issues and improve medical quality through decision support systems.