| 摘要: | 冠狀動脈繞道手術（CABG）對冠狀動脈疾病（CAD）患者是一種有用的治療方法。在一些相關研究中，潛在疾病和合併症會影響死亡率和再入院率，這些問題將直接增加醫療費用。但是，目前沒有研究找出會影響存活以
及醫療費用的危險因子。
本研究使用國家健康保險研究數據庫（NHIRD），健保資料庫是全台灣最大且最完整的資料庫，包含各種醫學信息、病患的門診、住院資訊。本研究中選擇首次接受CABG 手術的患者，並使用不同的機器學習演算法(LGR,
LR, CART, MARS, RF, SVR, XGBoost)透過特徵選取，找出影響存活以及費用的相關危險因子，再進行預測以及評估。
本研究結果顯示透過特徵篩選有助於提高模型預測率、準確性且可以利用較少的危險因子進行預測。CABG 患者共病患有腎臟疾病是影響存活的關鍵因子且腎臟疾病的病患醫療費用較高; 術前一年醫療費用、當次手術費用
以及洗腎的次數是預測術後一年費用的關鍵因子。本研究可以幫助政府制定良好的醫療政策，並可以朝著準確的預防性醫療、降低醫療總費用，及更有效的醫療管理的方向發展。
Coronary artery bypass surgery grafting (CABG) is a useful treatment for
patients with coronary artery disease (CAD). In some related studies, underlying
disease and comorbidities will affect mortality and readmission that will directly
increase medical expenses. However, the researches currently barely focus on
figuring out the most important risk factor that may affect the survival rate and
medical expense of the CAGB patients
This study used the National Health Insurance Research Database (NHIRD),
which is the largest dataset that includes comprehensive medical information, and
inpatients and outpatients diagnose records in Taiwan. We have selected patients
who received their CABG surgery for the first time and have used different
machine-learning algorithms, including: LGR, LR, CART, MARS, RF, SVR, and
XGBoost for selecting the most important variables, then evaluated and predicted
the major factors that impact survival rate and medical expenditure.
From this study, it shows that feature selection can help improve the prediction
capabilities and accuracy of the models with fewer factors. The result indicated
that kidney disease that CABG patients have is a risk factor that affect survival rate
and increase medical expenses. The medical expenditure in one-year before
surgery, surgical expense, and the times of HD were strong predictors of future
expense.
This research can help the government to formulate medical policies better.
Moreover, it enhances preventive medical prediction more precisely, reduces the
total medical expenses possibly, and develops more effective medical management. |
