Machine Learning Applications in Nursing-Affiliated Research: A Systematic Review

Abstract

Purpose: This study analyzed the methodological characteristics of machine learning (ML) applications in nursing research, evaluated their reporting quality against standardized guidelines, and assessed progress toward clinical implementation. Methods: A PRISMA-compliant systematic review (PROSPERO CRD42024595877) searched nine English- and Korean-language databases through September 27, 2024. Included studies applied ML to a nursing question and had at least one nursing-affiliated author. Two reviewers independently extracted data following the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Reporting quality was appraised using the TRIPOD+AI checklist. Results: Of 125 included studies, supervised learning predominated (93.6%), with random forest, logistic regression, and support vector machines as common algorithms. The most frequent performance metrics were the area under the receiver operating curve and accuracy. Mean TRIPOD+AI compliance was 50.4% (standard deviation=9.37), with reporting quality lowest for data preparation (48.0%) and class imbalance handling (22.4%). Research focused on predicting pressure injuries, falls, and readmissions. Only seven studies described clinical deployment, often citing ethical or workflow barriers. Conclusion: While ML studies in nursing are increasing and show strong discriminatory accuracy, their impact is limited by inconsistent reporting, limited external validation, and rare clinical deployment. Translating these algorithms into practice requires adopting comprehensive reporting guidelines like TRIPOD+AI, documenting each CRISP-DM phase, and integrating nurse-centered decision-support pathways.

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