Explainable machine learning applied to ECG-derived biomarkers may provide an objective method for pain assessment, potentially improving equity in pain management.
ECG-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.
Sabbadini et al. (Thu,) studied this question.