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

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 lea...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2022-07, Vol.2022, p.1-9
Main Authors: Huang, Way-Ren, Tseng, Ruo-Chia, Chu, Woei-Chyn
Format: Article
Language:English
Subjects:
Accident prevention
Algorithms
Artificial intelligence
Artificial neural networks
Bayesian analysis
Decision analysis
Decision making
Decision trees
Falls
Feature selection
Health care
Health services
Hospitalization
Hospitals
Machine learning
Medical errors
Patient safety
Prediction models
Risk
Risk assessment
Safety
Statistical analysis
Variables
Citations: Items that this one cites
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Summary: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.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/1869252