Enhancing Community-Based Nursing Decision Support: Machine Learning Models for Diabetes Risk Prediction Using Home Health Nursing Notes.

OBJECTIVES

This study aimed to identify high-risk factors for type 2 diabetes and develop a machine learning (ML)-based diabetes prediction model using nursing notes from home health care.

DESIGN

Retrospective cohort study.

SAMPLE: A total 7896 medical records from 1747 patients aged ≥ 20 years who received home health care at a university hospital in South Korea over 10 years.

MEASUREMENTS: The patient's sociodemographic characteristics, diagnosis codes, and clinical history as well as narrative nursing notes detailing nursing assessments and descriptions of the patient's condition were recorded. Logistic regression was used to identify risk factors, and five machine learning models were constructed using 10-fold cross-validation.

RESULTS

Female sex, depression, hypertension, hyperlipidemia, nursing services such as dressing and nutritional care, and dysuria were identified as prediabetes-related symptoms extracted from nursing notes using natural language processing (NLP). The Random Forest model demonstrated the highest predictive performance (AUC = 0.985).

CONCLUSIONS

Nursing documentation is a valuable resource for early diabetes risk screening. High predictive performance was achieved by integrating structured and unstructured data, prediabetes-related symptoms extracted from nursing notes. This approach enhances nurses' ability to deliver timely, personalized interventions and improve preventive care outcomes, with significant benefits for patients receiving home care who require continuous support.