Advancing Patient-Centric Care Through Genomic Data and Deep Learning

Abstract

Personalized medicine use genomic information and data analysis in order to develop treatment plan applicable to the specific patient. This approach is on the rise as the method to sequence DNA becomes more efficient and enabled by state-of-art deep-learning algorithms. Within the paradigm of genetic variations, personalized medicine can decrease side effects that occur during the treatment process as well as increase its efficiency. This approach is opposite to the mass approaches in the traditional system where the treatment is the same for all patients. This approach involves sophistication of deep learning models that increase the precision of risk, biotherapeutic interventions and genomics’. It also combines genetic data with demographic and medical data to create extensive client portraits. The suggested approach can be considered as a powerful basis for the translation of the concept of the PM into clinical reality. It points to the change that has been experienced in recent years regarding ownership of health data and deep learning to improve patient experience. The further research in this field has to concentrate on the potential solutions with the development of the algorithmic methods, and the repeatability and reproducibility of the results as well as the author’s ethical approach to work.

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