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Applying Machine Learning Techniques to Implementation Science
Nathalie Huguet;
Jinying Chen;
Ravi B. Parikh;
Miguel Marino;
Susan A. Flocke;
Sonja Likumahuwa-Ackman;
Justin Bekelman;
Jennifer E. DeVoe
ABSTRACT
Machine learning approaches can be an innovative and useful tool in implementation science. In this viewpoint, we introduce a roadmap for applying machine learning techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support; what and when adaptation and/or de-implementation are needed. We describe experiences learned from real-world applications of machine learning in implementation science, and discuss challenges that implementation scientists will need to consider when using machine learning throughout the stages of implementation.
Citation
Please cite as:
Huguet N, Chen J, Parikh RB, Marino M, Flocke SA, Likumahuwa-Ackman S, Bekelman J, DeVoe JE
Applying Machine Learning Techniques to Implementation Science