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Related Experiment Video

Updated: Jun 29, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Classifying early infant feeding status from clinical notes using natural language processing and machine learning.

Dominick J Lemas^1,2, Xinsong Du^3,4, Masoud Rouhizadeh^5,6

¹Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA. djlemas@ufl.edu.

Scientific Reports

|April 3, 2024

Summary

This summary is machine-generated.

This study developed natural language processing (NLP) models to accurately predict infant feeding status from electronic health records. The XGBoost model achieved 90.1% accuracy, enabling early identification for public health interventions.

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Area of Science:

Computational linguistics
Health informatics
Machine learning

Background:

Accurate infant feeding status is crucial for public health.
Electronic health records (EHRs) contain valuable unstructured clinical notes.
Predicting feeding status from EHRs can inform targeted interventions.

Purpose of the Study:

To develop and evaluate natural language processing (NLP) and machine learning (ML) models.
To predict infant feeding status using clinical notes from the Epic EHR system.
To classify feeding status using Medical Subject Headings (MeSH) terms.

Main Methods:

Trained six ML models (logistic regression, random forest, XGBoost, k-NN, SVC) on 999 manually reviewed clinical notes.
Utilized TeamTat for annotation and MeSH terms for classification.
Evaluated models based on accuracy, precision, recall, and F1 score.

Main Results:

The XGBoost model demonstrated superior performance with 90.1% accuracy.
Achieved macro-averaged precision, recall, and F1 score of 90.3%, 90.1%, and 90.1%, respectively.
Successfully classified infant feeding status into breast, formula/bottle, and missing categories.

Conclusions:

NLP is effective for classifying infant feeding status from unstructured EHR data.
Early identification via NLP can support precision public health initiatives.
Improved lactation support for postpartum patients can be facilitated by these findings.