Dominick J Lemas, Xinsong Du, Masoud Rouhizadeh, Braeden Lewis, Simon Frank, Lauren Wright, Alex Spirache, Lisa Gonzalez, Ryan Cheves, Marina Magalhães, Ruben Zapata, Rahul Reddy, Ke Xu, Leslie Parker, Chris Harle, Bridget Young, Adetola Louis-Jaques, Bouri Zhang, Lindsay Thompson, William R Hogan, François Modave
The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier...
April 3, 2024: Scientific Reports