Applying Contemporary Machine Learning Approaches to Nutrition Care Real-World Evidence: Findings From the National Quality Improvement Data Set

Abstract

Using real-world data from the Academy of Nutrition and Dietetics Health Informatics Infrastructure, we use state-of-the-art clustering techniques to identify 2 phenotypes characterizing the episodes of nutrition care observed in the National Quality Improvement (NQI) registry data set. The 2 phenotypes identified from recorded Nutrition Care Process data in the NQI exhibit a strong correspondence with the clinical expertise of registered dietitian nutritionists. For one of these phenotypes, it was possible to implement state-of-the-art classification techniques to predict the nutrition problem-resolution status of an episode of care. Prediction results show that the assessment of nutrition history, number of recorded visits in the episode, and use of nutrition counseling interventions were significantly and positively correlated with problem resolution. Meanwhile, evaluations of nutrition history that were not within the desired ranges were significantly and negatively correlated with problem resolution. Finally, we assess the usefulness of the current NQI data set and data model for supporting the application of contemporary machine learning methods to the data set. We also suggest ways of enhancing the NQI since registered dietitian nutritionists are encouraged to continue to contribute patient cases in this and other registry nutrition studies.