|摘要: ||傳統在全民健保資料庫的分析研究上，大都從單一特定疾病統計，或共病性疾病間的網絡著手。 近年來，使用人工智慧分析大數據，且應用在醫療領域上已較過往更為可信。 當人工智能學習了中醫十年 (2004-2013年) 健保資料庫門診開立處方的數據，而能預測中醫處方所屬年份的分群現象，可供當作此研究的起始點。|
本論文研究，接續了2017年《確認與中醫處方有關的環境和社會經濟變數》初步研究結果中，利用階層式分群法得到社會總體經濟指標-國內生產毛額、國民所得毛額，與AI預測處方年份分群有相似的結果。 進而引發蒐集更多具代表性社會經濟變量，來確認影響此分群現象最為關鍵的因子，並新增了變量關係網絡分析法協助確認。 總共收集- 國民所得、家庭收支、物價統計、衛生統計，教育及環保統計六大類，合計有2500項變量。 結果顯示，其中符合統計檢定有19項，三分之一與教育方面有關，包含教育服務業生產總值 (毛額)、受雇人員報酬，並政府經費運用國人教育等各方面。 進一步把這19項變量計算相關係數並繪製網絡圖。 得到教養設備及用具、發電機、院所病床數、醫療保健服務業、行動電話，傳播業及教育服務業這七項變量，在關係網絡圖中佔有重要的位置及功能。
越來越多的研究實證，開發中國家的教育與人口健康議題，一直存有正向的關係。 但通常有時間延遲及衡量上的困難。 近日更有回顧性文章整理出，成人或全人教育比單靠經濟因素，更有直接且明顯影響全民健康的效果。 因此，從本研究中，亦可佐證政府愈重視全民的教育，愈能提升人民的健康狀態。;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.