AI-DRIVEN PERSONALIZED MEDICINE: TAILORING TREATMENTS THROUGH GENOMIC AND CLINICAL DATA INTEGRATION

Abstract

Personalized medicine uses genomic and clinical data to tailor treatment regimens specific to the individual. AI provides enabling resources for integration, and analysis of multi-modal data, to get you to highly precise, individualized therapy. We rationally derive an AI predictive model by integrating genomic variants with clinical parameters to guide drug selections in patients with hypertension. We trained machine learning models to predict drug response from a dataset of 500 patients. The Random Forest classifer, which classified AKI development with 87% accuracy, was used to obtain natural genomic markers and clinical factors affecting treatment outcomes. Findings show how AI could change personalized medicine by offering data-driven, patient-specific management plans. Background: Large amounts of clinical and genomic data data are being generated but we still cannot make accurate predictions of health