An Economic Decision-Making Model for Drugs Using Big Data and Convolution Neural Network in Healthcare

Abstract

Incorporating health big data (HBD) and observational real-world patient-level genomic and clinical data in several subpopulations into drug economic analysis of precision medicine has the potential to be beneficial ion various ways. However, health economists encounter practical and operational difficulties when using HBD in this framework. On an individual patient level, the “big data” age represents an exciting opportunity to use strong new sources of information to reduce clinical and drug economic uncertainty. Health big data is a very important resource in the field of medicine. It is an irresistible trend to rationally utilize the advantages of health big data to serve medical care and clinical medicine. Health big data will play an irreplaceable role in pharmaceutical research and development, disease diagnosis and treatment, health risk factor analysis, public health emergency management, residents’ health management, and precision medicine. However, the application of health big data in the field of medicine also faces some challenges, such as irrational drug use in primary medical and health institutions. This paper builds a drug economic decision-making model based on health big data and uses a convolutional neural network to study the model. This research is conducted to realize the understanding of drug demand, which is conducive to the country’s understanding of drugs. It also allows people to better seek medical treatment. Artificial intelligence’s new advancements in the big data era have prepared the way for future rational drug modeling and evaluation that will have a huge influence on drug discovery methods which ultimately have better impact on public health.