Personalized Treatment Suggestions Based on Multiple Types of Patient Data
DOI:
https://doi.org/10.53469/ijomsr.2025.08(04).10Keywords:
Personalized medicine, AI-assisted decision-making, Medication recommendation system, Pharmacokinetics, Electronic medical record miningAbstract
In the context of rapid progress in precision medicine, personalized drug strategies play a vital role in improving treatment outcomes and reducing the risk of adverse drug reactions. This study developed an AI-based personalized medication recommendation system that integrates electronic medical records, genomic data, and pharmacokinetic parameters. Using deep neural networks and ensemble learning models, the system provides intelligent and accurate recommendations for drug selection and dose adjustment. To improve model interpretability, we introduced feature importance analysis, which significantly enhanced the system's transparency. Prospective validation on real-world clinical datasets shows that the system outperforms traditional rule-based methods and single algorithms in both drug matching accuracy and dose prediction error control. For example, in antihypertensive drug recommendation tasks, the system achieved an accuracy of 91.2%, and the average dosage deviation was controlled within ±8.3%. This research offers a practical technical pathway for implementing AI-based personalized medication systems in clinical settings and shows strong potential for broader clinical use.
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