报告题目：Data driven, two stage machine learning algorithm based prediction scheme for assessing 1 year and 3 year mortality risk in chronic hemodialysis patients

报告所属学科：管理科学与工程

报告人：陳銘芷（輔仁大學）

报告时间：2024年4月17日 10:00-12:00

报告地点：经管学院702室

报告摘要：

Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.

报告人简介：

陳銘芷，德州農工大學博士，農工大學工業工程博士後，並至日本愛知工業大學學術交流，曾擔任台灣人工智慧發展學會副理事長、理事，目前為輔仁大學管理學院商研所教授兼所長，及輔仁大學人工智慧發展中心執行長，主要研究領域為可靠度與維護度，資料探勘及機器學習演算法應用。最近研究專注與醫師合作分析醫療數據，已經合作發表多篇應用機器學習演算法找出疾病風險因子，其中兩篇肺癌與食道癌論文於 Radiology 及 Journal of Thoracic Oncology，期刊排名前10%，且 Impact factor 高於20分。