US National Institutes of Health described the precision medicine as ‘an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person.’ In other words, on the basis of the definition, the precision medicine allows to treat patients based on their genetic, lifestyle, and environmental data. Nevertheless, the complexity and rise of data in healthcare arising from cheap genome sequencing, advanced biotechnology, health sensors patients use at home, and the collection of information about patients’ journey in healthcare with hand-held devices unquestionably require a suitable toolkit and advanced analytics for processing the huge information. The artificial intelligence algorithms (AI) can remarkably improve the ability to use big data to make predictions by reducing the cost of making predictions. The advantages of artificial intelligence algorithms have been extensively discussed in the medical literature. In this paper based on the collection of the data relevant for the health of a given individual and the inference obtained by AI, we provide a simulation environment for understanding and suggesting the best actions that need to be performed to improve the individual’s health. Such simulation modelling can help improve clinical decision-maing and the fundamental understanding of the healthcare system and clinical process.
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