ArticlePain research & management2026
Machine Learning and ECG-Derived Biomarkers for Objective Pain Assessment: An Explainable AI Approach Toward Precision Pain Management.
Article in Pain research & management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Machine Learning and ECG-Derived Biomarkers for Objective Pain Assessment: An Explainable AI Approach Toward Precision Pain Management.Pain research & management · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundPain assessment disparities across demographic groups pose a significant healthcare challenge, with women, elderly patients, and racial minorities frequently experiencing inadequate treatment. Current pain evaluation relies predominantly on subjective self-reporting scales, which lack objective physiological biomarkers and can be influenced by communication barriers and provider bias, limiting their effectiveness for equitable and precise pain measurement.
objectivesThis study investigates the development and validation of ECG-derived biomarkers for objective pain assessment through machine learning enhanced with explainable artificial intelligence (XAI), establishing a foundation for precision pain management that addresses existing healthcare disparities.
methodsWe implemented a dual-model machine learning architecture incorporating SHapley Additive exPlanations (SHAP) for transparent pain assessment using ECG biomarkers. The system employs a Random Forest Classifier for binary pain detection (CoVAS = 0 vs. CoVAS > 0) and a Random Forest Regressor for continuous pain intensity estimation (0-100 scale). Comprehensive feature extraction from ECG signals captured time-domain, frequency-domain, and heart rate variability parameters as candidate pain biomarkers.
resultsThe classification model achieved 85.2% accuracy in distinguishing pain presence from pain-free states. Key validated biomarkers included ECG root mean square (RMS), peak-to-peak amplitude, and heart rate variability metrics. SHAP analysis provided transparent, interpretable insights into biomarker contributions, revealing complex interactions among cardiac parameters that reflect underlying pain physiology and establishing the explainability necessary for clinical trust and adoption.
conclusionsECG-derived biomarkers analyzed through explainable machine learning offer a pathway toward objective, transparent pain assessment capable of addressing disparities in pain management. This approach identifies reliable physiological pain indicators from routinely collected cardiac signals, potentially enabling precision pain management in clinical settings where comprehensive multimodal monitoring systems are unavailable, thereby supporting more equitable healthcare delivery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.