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Using Machine Learning Technologies in Pressure Injury Management: Systematic Review.

Mengyao Jiang¹, Yuxia Ma¹, Siyi Guo²

¹Evidence-based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China.

JMIR Medical Informatics

|March 10, 2021

Summary

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Machine learning (ML) shows promise in pressure injury (PI) management by aiding prediction, posture detection, and image analysis. Further large-scale clinical studies are needed to confirm effectiveness and improve methodological quality.

Area of Science:

Medical Technology
Artificial Intelligence in Healthcare
Clinical Informatics

Background:

Pressure injuries (PI) pose significant challenges due to nurse shortages and knowledge gaps.
Machine learning (ML) offers potential solutions for improving PI prognosis and diagnostic accuracy.
A systematic review evaluating ML applications in PI management is currently lacking.

Purpose of the Study:

To synthesize and evaluate the existing literature on ML technologies in PI management.
To identify the strengths and weaknesses of current ML applications in PI care.
To highlight opportunities for future research and clinical practice improvements.

Main Methods:

An extensive literature search was conducted across multiple databases (PubMed, EMBASE, CINAHL, etc.) in June 2020.

Keywords:

Naive Bayes artificial intelligence bayesian learning bedsore boosting computational intelligence computer reasoning decubitus sore decubitus ulcer deep learning machine intelligence machine learning management natural language processing neural network pressure damage pressure injuries pressure sore pressure ulcer random forest support vector support vector machine systematic review

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Two independent investigators performed study selection, data extraction, and quality appraisal.

Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Main Results:

32 articles met the inclusion criteria, focusing on ML for PI management.
ML was used for predictive modeling (38%), posture detection (34%), and image analysis (28%).
The overall risk of bias across the studies was judged as high.

Conclusions:

Emerging ML technologies show promising laboratory results for PI management.
Future research should focus on large-scale clinical data validation to enhance ML effectiveness.
Improving the methodological quality of ML studies in PI is crucial for clinical translation.