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Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques.

Ya-Han Hu¹, Yi-Lien Lee, Ming-Feng Kang

¹Author Affiliations: Department of Information Management, National Central University, Taoyuan, Taiwan (Dr Hu); Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Chiayi, Taiwan (Dr Hu); MOST AI Biomedical Research Center, National Cheng Kung University, Tainan, Taiwan (Dr Hu); Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi, Taiwan (Ms Lee, Ms Kang, and Dr Lee); Department of Medical Affairs, Chi Mei Medical Center, Tainan, Taiwan (Ms Lee); and Office of Resource Management, St. Martin De Porres Hospital, Chiayi, Taiwan (Ms Kang).

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Summary

This summary is machine-generated.

Machine learning models predict inpatient pressure injuries. The random forest model showed the best performance, identifying key risk factors like skin integrity and blood pressure for early prevention.

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Area of Science:

Nursing Quality Indicators
Clinical Informatics
Predictive Analytics in Healthcare

Background:

Pressure injuries are a significant concern in clinical care, impacting patient outcomes and healthcare costs.
Existing pressure injury risk assessment scales lack universal applicability across different healthcare systems.
Early identification and management of pressure injury risk factors are crucial for patient well-being and resource optimization.

Purpose of the Study:

To develop and evaluate machine learning models for predicting inpatient pressure injuries.
To identify critical risk factors associated with pressure injury development in hospitalized patients.
To improve the accuracy and generalizability of pressure injury risk assessment.

Main Methods:

Utilized machine learning techniques: decision tree, logistic regression, and random forest.
Analyzed a dataset comprising 11,838 inpatient records.
Trained and evaluated three distinct prediction models using 30 sets of training samples.

Main Results:

The random forest model demonstrated the highest classification performance with an accuracy of 0.845.
Identified key predictors of pressure injury: skin integrity, systolic blood pressure, expression ability, capillary refill time, and level of consciousness.
The developed models offer a data-driven approach to pressure injury risk assessment.

Conclusions:

Machine learning, particularly random forest, provides a robust method for predicting inpatient pressure injuries.
The identified risk factors offer valuable insights for targeted prevention strategies.
This study contributes to the advancement of predictive analytics in nursing and patient safety.