| dc.description.abstract | The conventional way to employ National Health Insurance Research Database (NHIRD) focuses on single specific disease statistics or comorbidity study by network analysis. In recent years, the use of artificial intelligent (AI) to analyze big data and develop applications in medical field has become more credible. When AI trained with ten years (2004-2013) traditional Chinese medicine (TCM) prescription data extracted from NHIRD, it acquired capability to group TCM prescriptions into clustered years. Therefore we took this as the initiative of our current studies.
In this research, continuous to our prior study of “Identification of environmental and socioeconomic variables that are associated with TCM prescriptions” in 2017, we generated hierarchical clustering-derived data patterns for overall social economic indicators--gross domestic product and gross national income, which are similar to the AI-predicted data patterns for TCM prescriptions grouped by years. As a result, it led us to collect more representative socioeconomic variables to identify the mostly influential factors correlated with the clustering patterns of TCM prescriptions. A new method of network analysis to dissect variable relationships was introduced to facilitate the validation. A total of 2,500 variables in 6 categories, including national income, household income and expenditure, retail price, healthcare, education and environmental protection statistics, were collected. The results showed that there are 19 variables meet the statistical significance, nearly one-third of which are related to education statistics, including education service gross production value, employee income, government funding for national education. These 19 variables were further computed for correlation co-efficiencies and graphed for network mapping. Subsequently, seven indicators as education equipment and device, power generator, ward bed counts, healthcare service, mobile phone, mass communication and education service were weighted to have more important positions and functions in correlation network.
A growing body of research has shown that education and population health issues always exist in a positive correlating manner; however, there are often time delays in such measuring. Moreover, recent retrospective articles have compiled that continuous education or holistic education has more direct and obvious effects on population health than economic factors alone. Therefore, our results are further supportive to the belief that the more the government attaches importance to the development of national education, the more it can improve the health of the people. | en_US |