Using AI and Big Data in the HealthCare Sector to help build a Smarter and more Intelligent HealthCare System

Using AI and Big Data in the HealthCare Sector to help build a Smarter and more Intelligent HealthCare System

연구 분야: Artificial Intelligence

학회: 2024 IEEE World AI IoT Congress (AIIoT)

The purpose of this paper is to demonstrate the use of AI and Big Data in HealthCare Sector to help build a smarter and more intelligent HealthCare System. Researchers in HealthCare Sectors are relying heavily on big data and compute power to build correlations by using statistical methods and artificial intelligence (AI) models. These models enable Healthcare Sector participants to manage HealthCare for a core set of the population. They also help providers to analyze the impact of decisions on their most vulnerable patients. There are many factors that are considered in performing big data analysis, some of them are: the patient’s medical history, genetic information, eating habits and fitness regimen. The data that is analyzed includes several key decision-making processes. Some of the challenges with the data used include data quality, data validation, data knowledge, domain expertise, and data integration challenges with various end points. While performing data analysis, the HealthCare Sectors must take security and data governance (HIPPA regulations etc.) into consideration. Big data analysis follows the (4P) approach [1], preference, prediction, personalization, and promotion. The question that arises most often is the type of data that is the most reliable for analysis in the HealthCare Sector. Most HealthCare organizations use demographic information, diagnosis, treatment, prescription drugs, laboratory tests, physiologic monitoring data, hospitalization, and patient insurance for their analysis. Since the data comes from multiple sources [2], there is a big challenge to perform data integration, extraction, and transformation as it consumes large amounts of resources and compute power, coupled with the additional challenges of data aggregation, data enrichment and format inconsistencies. To address this challenge and to analyze the process completely requires data scientists who have domain knowledge and expertise to extract, enrich and transform data. an... Show More