A comparative study of anti-hypertensive drugs prediction models for the elderly based on machine learning algorithms

A comparative study of anti-hypertensive drugs prediction models for the elderly based on machine learning algorithms

Our team has built a model framework using measurements of concentration of drugs in human blood combined with artificial intelligence and models of pharmacometrics. Pharmacometrics can be defined as that branch of science concerned with mathematical models of biology, pharmacology, disease, and physiology used to describe and quantify interactions between drugs and patients, including beneficial effects and adverse effects. In order to ensure the accuracy and generalization of the model, quantities of individual parameters and clinical data were desired to train the model, thus representing the real differences of populations and individuals. Our current purpose is to establish a virtual human model covering adults and seniors (18 to 75 years old) by reinforcement learning. Reinforcement learning (RL) is a kind of machine learning (ML) technique that trains software to make decisions to achieve the best results. It mimics the trial-and-error learning process that humans take to achieve their goals. Software operations that help achieve the goal are enhanced, while actions that deviate from the goal are ignored. After training on a large number of real individual data and drug data, the virtual human body will generate a large model of pharmacokinetics-pharmacodynamics, which can generate high-quality prediction results. The virtual human model can simulate the interaction between anti-hypertensive drugs and hypertension populations according to the real drug exposure and individual physiological environment. Doctors can better understand the hypertension patient's physical condition at the time of medication, thus they can develop a more personalized medication plan for them according to the results of the model fitting to improve the treatment effect and reduce the occurrence of adverse reactions. This model has great potential to promote the process of precision medicine.

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