Establishing a Prediction Model by Machine Learning for Accident-Related Patient Safety

Abstract

Patient safety has always been an important issue when improving the quality of medical care. The first step when preventing accidents is to screen for the high-risk groups that are prone to accidents. A patient safety reporting system is one of the best tools for such screening. We used machine learning techniques to analyze events involving falling and establish a risk prediction model. The results are then fed back to medical organizations with the aim of raising their quality of medical care. Bayesian network, artificial neural network, logistic regression, and decision tree-based chi-square automatic interaction detection were applied to analyze a database covering a 14-month period from November 2012 to December 2013, and the area under the ROC curve (AUC) values were 0.940, 0.979, 0.981, and 0.971, respectively. Next, data from January to February 2013 was verified by the model showing the highest discrimination ability, namely, logistic regression, and the AUC and F -measure values were 0.944 and 0.871, respectively. Our results demonstrate that a high level of performance was obtained when using a machine learning model for fall prediction among newly admitted patients. Machine learning can thus be applied to provide the most appropriate decision-aided prediction model when screening high-risk groups that are prone to accidents within hospitals.

Enhanced Multi-Stage Guided-Attention Mechanism for Traffic Accident-Based Patient Safety Prediction Model

“…The feature selection receives the preprocessed data. By taking the Pearson correlation technique into account, the feature selection is accomplished [21]. Finally, a proposed classi er is used to identify patient safety.…”