SynthesisJournal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing2026
Machine Learning Models for Predicting Pain, Fatigue, Depression, Anxiety, and Malnutrition in Cancer Patients: A Systematic Review and Meta-Analysis.
Synthesis in Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionCancer-related symptoms including pain, fatigue, depression, anxiety, and malnutrition drive poor quality of life and adverse clinical outcomes in cancer patients. While machine learning (ML) models are increasingly developed to predict these symptoms, existing studies are marked by significant heterogeneity in algorithms, sample sizes, and predictors, and lack quantitative synthesis of model performance, methodological quality, and clinical applicability. This study aimed to comprehensively summarize the characteristics of models and predictors, evaluate the predictive accuracy, risk of bias, and clinical applicability of ML prediction models.
designSystematic review and meta-analysis.
methodsA comprehensive literature search was conducted in PubMed, Web of Science, the Cochrane Library, CINAHL, PsycINFO, CNKI, WanFang, VIP, and SinoMed, from database inception to August 31, 2025. Data were extracted in accordance with the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), and the risk of bias and applicability of included models were assessed using the Prediction Model Risk of Bias Assessment Tool and Artificial Intelligence (PROBAST-AI). The quality of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. A random-effects model was employed for pooled analysis. Subgroup analyses were stratified by cancer type, geographic region, and algorithm type.
resultsA total of 11,217 records were retrieved, and 34 studies were included in the analysis. The pooled AUCs for predicting pain, fatigue, depression, anxiety, and malnutrition were 0.76 (95% CI: 0.69-0.83, I
conclusionThis systematic review and meta-analysis showed that ML models achieved acceptable discriminative performance for predicting pain, anxiety, depression, fatigue, and malnutrition in patients with cancer in available datasets. Given predominant internal validation and observed heterogeneity, clinical utility requires further prospective validation and implementation studies. Future research may consider theory-driven predictors and clinically tailored algorithms to improve model performance. CLINICAL RELEVANCE: These pooled findings provide a preliminary foundation for the clinical translation of ML models to predict pain, anxiety, depression, fatigue, and malnutrition in cancer patients. Further prospective validation in diverse clinical settings and randomized controlled trials evaluating the effectiveness of model-guided symptom management strategies are needed to improve patient outcomes. PROSPERO REGISTRATION: CRD420251130183.
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