Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation
JMIR Medical Informatics2020Vol. 8(11), pp. e19489–e19489
Citations Over TimeTop 10% of 2020 papers
Tahmina Nasrin Poly, Md. Mohaimenul Islam, Muhammad Solihuddin Muhtar, Hsuan‐Chia Yang, Phung‐Anh Nguyen, Yu‐Chuan Li
Abstract
In this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication-related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.
Related Papers
- → Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction(2019)71 cited
- → Artificial Intelligence, Machine Learning, and Medicine: A Little Background Goes a Long Way Toward Understanding(2021)29 cited
- → Application of Machine Learning in Animal Disease Analysis and Prediction(2020)26 cited
- → Sentiment Analysis by Using Supervised Machine Learning and Deep Learning Approaches(2020)3 cited
- → Breakdown of Machine Learning Algorithms(2022)1 cited