Electronic health record (EHR) data must be mapped to standard information models for interoperability and to support research across organizations. New information models are being developed and validated for data important to nursing, but a significant problem remains for how to correctly map the information models to an organization’s specific flowsheet data implementation. This paper describes an approach for automating the mapping process by using stacked machine learning models. A first model uses a topic model keyword filter to identify the most likely flowsheet rows that map to a concept. A second model is a support vector machine (SVM) that is trained to be a more accurate classifier for each concept. The stacked combination results in a classifier that is good at mapping flowsheets to information models with an overall f2 score of 0.74. This approach is generalizable to mapping other data types that have short text descriptions.

Learning Objective: Understand the issues with mapping flowsheet data that only have short text descriptions to standard information models and how machine learning can be used to automate the mapping process.

Authors:

Steven Johnson (Presenter)
University of Minnesota

Lisiane Pruinelli, University of Minnesota
Bonnie Westra, University of Minnesota

Presentation Materials:

• Abstract/Manuscript - 1