A proposed protocol for personalised prediction of opioid response in oncology patients with chronic pain using machine learning methods combining genetic and clinical factors

Le résumé fourni par la source

Opioids are a cornerstone treatment for chronic pain in oncology patients, yet individual response varies widely due to genetic differences in CYP2D6 enzyme activity, which governs the metabolism of codeine and tramadol. Patients are classified into four metaboliser phenotypes, each associated with distinct efficacy and side-effect profiles. The current study proposes a research protocol integrating genetic and clinical data through machine learning models (WEKA, Omics-CNN) to predict individual opioid response. Patients from the Hippocration Pain Unit will undergo CYP2D6 genotyping and standardized pain assessment. The aim is to develop a predictive tool to support personalised, evidence-based opioid prescribing in cancer pain management.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.