Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods
JMIR Research Protocols2017Vol. 6(8), pp. e175–e175
Citations Over TimeTop 10% of 2017 papers
Gang Luo, Bryan L. Stone, Michael D. Johnson, Peter Tarczy‐Hornoch, Adam Wilcox, Sean D. Mooney, Xiaoming Sheng, Peter J. Haug, Flory L. Nkoy
Abstract
Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes.
Related Papers
- → Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction(2019)71 cited
- Big Data Mining and Semantic Technologies: Challenges and Opportunities(2015)
- → A Study on Big Data Analytics Tools(2023)2 cited
- → Breakdown of Machine Learning Algorithms(2022)1 cited
- → Deciphering Big Data Research Themes(2018)