عنوان انگلیسی مقاله:
Machine Learning for Health Services Researchers
ترجمه فارسی عنوان مقاله:
یادگیری ماشین برای محققان خدمات بهداشتی
Sciencedirect - Elsevier - Value in Health, 22 (2019) 808-815: doi:10:1016/j:jval:2019:02:012
Patrick Doupe, PhD,1,* James Faghmous, PhD,2 Sanjay Basu, MD, PhD3,4
Background: Machine learning is increasingly used to predict healthcare outcomes, including cost, utilization, and quality.
Objective: We provide a high-level overview of machine learning for healthcare outcomes researchers and decision makers.
Methods:We introduce key concepts for understanding the application of machine learning methods to healthcare outcomes
research. We first describe current standards to rigorously learn an estimator, which is an algorithm developed through
machine learning to predict a particular outcome. We include steps for data preparation, estimator family selection,
parameter learning, regularization, and evaluation. We then compare 3 of the most common machine learning methods: (1)
decision tree methods that can be useful for identifying how different subpopulations experience different risks for an
outcome; (2) deep learning methods that can identify complex nonlinear patterns or interactions between variables predictive
of an outcome; and (3) ensemble methods that can improve predictive performance by combining multiple machine
Results: We demonstrate the application of common machine methods to a simulated insurance claims dataset. We specifically
include statistical code in R and Python for the development and evaluation of estimators for predicting which patients
are at heightened risk for hospitalization from ambulatory care-sensitive conditions.
Conclusions: Outcomes researchers should be aware of key standards for rigorously evaluating an estimator developed
through machine learning approaches. Although multiple methods use machine learning concepts, different approaches are
best suited for different research problems.
Keywords: claims data | deep learning | elastic net | gradient boosting machine | gradient forest | health services research | machine learning | neural networks | random forest