Machine Learning-Ready Genomic Biomarkers: ATF3 Polymorphisms Predict Postoperative Analgesic Demand Through AI-Compatible Phenotyping.

Postoperative pain management remains a major challenge due to substantial interindividual variability, and integrating genetic biomarkers with artificial intelligence (AI) offers a promising approach to precision analgesia. In this study, we investigated stress-responsive transcription factor ATF3 polymorphisms within a machine learning–ready framework to predict postoperative analgesic requirements. In a prospective cohort of 167 adults undergoing abdominal surgery, homozygous carriers of ATF3 SNPs rs3122721 and rs3125293 showed significantly higher opioid consumption independent of subjective pain scores, establishing a robust genotype–phenotype association suitable for algorithmic modeling. To enable translation, we developed a structured dataset architecture that supports real-time predictive analytics, allowing genetic profiles to serve as input features for deep learning models capable of forecasting high-risk patients and guiding personalized therapeutic strategies. These findings revealed persistent genotype-dependent opioid requirements over 72 h, providing a biological basis for clinical decision support systems that can dynamically adjust PCA protocols and dosing recommendations while incorporating safeguards to minimize opioid-related adverse effects. Collectively, this work identifies ATF3 genotyping as a promising biomarker for AI-driven precision analgesia, bridging genomic insight with actionable clinical strategies, and highlights practical considerations for perioperative integration along with the ethical implications of preoperative genetic testing.