Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Jiang, Mengyao; Ma, Yanhui; Guo, Siyi; Jin, Liuqi; Lv, Lin; Han, Lin; An, Ning

doi:10.2196/25704

Cited by 41 publications

(37 citation statements)

References 56 publications

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“…Some studies focused on the analysis of algorithms or models for predicting PU risk, usually having an associated risk indicator as an output (e.g., [14]). The study presented in [13] partially addresses this issue. In this study, the authors analyze works that used ML technologies with the aim of contributing to lessen the burden on medical staff by improving the prognosis and diagnostic accuracy of PU.…”

Section: Review Analysis/resultsmentioning

confidence: 94%

“…Identified Opportunities and Future Research [13] Studies were classified and organized into three groups: 12 (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PU wounds.…”

Section: Review Analysis/resultsmentioning

confidence: 99%

“…However, recommendations and alerts for decision support for healthcare professionals will only be useful if they also include input information from other tiers, such as data acquisition and data analysis. This issue was also identified in [13] and represents an important opportunity for future research.…”

Section: Challenges and Opportunitiesmentioning

confidence: 90%

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Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

et al. 2021

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Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities.

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Section: Review Analysis/resultsmentioning

confidence: 94%

Section: Review Analysis/resultsmentioning

confidence: 99%

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Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

et al. 2021

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“…The study presented in [7] is focused on ML applications for PU prevention. In this study, the authors analyze works that use ML technologies to lessen the burden of medical staff by improving the prognosis and diagnostic accuracy of PU.…”

Section: Review Analysis/resultsmentioning

confidence: 99%

“…To identify the state-of-the-art approaches that use software to assist health professionals in PU prevention support. 36 1989-2014 [7] 2021…”

Section: Reviewmentioning

confidence: 99%

A Review of Intelligent Sensor-Based Systems for Pressure Ulcer Prevention

et al. 2021

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Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and software approaches have been proposed to ameliorate some aspects of pressure ulcer prevention and monitoring. In this article, we focus on reviewing solutions that use sensor-based data, possibly in combination with other intrinsic or extrinsic information, processed by some form of intelligent algorithm, to provide healthcare professionals with knowledge that improves the decision-making process when dealing with a patient at risk of developing pressure ulcers. We used a systematic approach to select 21 studies that were thoroughly reviewed and summarized, considering which sensors and algorithms were used, the most relevant data features, the recommendations provided, and the results obtained after deployment. This review allowed us not only to describe the state of the art regarding the previous items, but also to identify the three main stages where intelligent algorithms can bring meaningful improvement to pressure ulcer prevention and mitigation. Finally, as a result of this review and following discussion, we drew guidelines for a general architecture of an intelligent pressure ulcer prevention system.

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A systematic review of predictive models for hospital‐acquired pressure injury using machine learning

et al. 2022

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Aims and objectives To summarize the use of machine learning (ML) for hospital‐acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high‐quality ML predictive models. Background As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study. Design Systematic review. Methods Relevant articles published between 2010–2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta‐analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool. Results Twenty‐three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149–75353; the prevalence of pressure injuries ranged from 0.5%–49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre‐processing and model validation. Conclusions ML, as a powerful decision‐making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre‐processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice. Relevance to Clinical Practice This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre‐processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision‐making tool. A complete and rigorous model construction process should be followed in future studies to develop high‐quality ML models that can be applied in clinical practice.

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Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Cited by 41 publications

References 56 publications

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

A Review of Intelligent Sensor-Based Systems for Pressure Ulcer Prevention

A systematic review of predictive models for hospital‐acquired pressure injury using machine learning

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