2024-08-16
【學術亮點】使用整合學習和分層策略來預測 ESWL 治療尿道結石的效果
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【學術亮點】使用整合學習和分層策略來預測 ESWL 治療尿道結石的效果
Intelligent Cultivation: Using physiological indicators to Establish a Smart Health Early Warning Platform for Crops【Institute of Genomics and Bioinformatics / Yen-Wei Chu / Professor】
智慧栽培：應用生理指標建立超前預警之作物栽培管理平台【基因體暨生物資訊學研究所 朱彥煒教授】
上架日期：2024/07/23
Intelligent Cultivation: Using physiological indicators to Establish a Smart Health Early Warning Platform for Crops【Institute of Genomics and Bioinformatics / Yen-Wei Chu / Professor】
智慧栽培：應用生理指標建立超前預警之作物栽培管理平台【基因體暨生物資訊學研究所 朱彥煒教授】
| 論文篇名 | 英文：Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment 中文：使用整合學習和分層策略來預測 ESWL 治療尿道結石的效果 |
| 期刊名稱 | Computers in Biology and Medicine |
| 發表年份, 卷數, 起迄頁數 | 2024, 179, 108904 |
| 作者 | Chi-Wei Chen, Wayne-Young Liu, Lan-Ying Huang, and Yen-Wei Chu(朱彥煒)* |
| DOI | 10.1016/j.compbiomed.2024.108904 |
| 中文摘要 | 尿道結石是一種常見且容易復發的醫療疾病。準確預測手術後的成功率可以避免無效的醫療，並減少不必要的醫療費用。本研究收集了接受體外震波碎石術的上尿道結石患者數據，包括首次和第二次碎石術後成功與未成功去除結石的病例，並構建了對首次和第二次碎石術結果的預測系統。特徵提取自：患者特徵、結石特徵和體外震波碎石機器數據，並使用特徵創建生成額外特徵。最後，使用六種方法計算特徵重要性，分析特徵對模型的影響。本研究首次提出碎石術預測模型從43種方法和7種集成學習技術中選出，達到了0.91的AUC。對於第二次碎石術，AUC達到0.76。結果表明，患者關於結石病史的詳細和二元信息對首次和第二次碎石術預測準確性的貢獻不同。該預測工具可在以下網址訪問：https://predictor.isu.edu.tw/ks |
| 英文摘要 | Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffective medical procedures and reduce unnecessary healthcare costs. This study collected data from patients with upper ureter stones who underwent extracorporeal shock wave lithotripsy, including cases of successful as well as unsuccessful stone removal after the first and second lithotripsy procedures, and constructed prediction systems for the outcomes of the first and second lithotripsy procedures. Features were extracted from three categories of information: patient characteristics, stone characteristics, and extracorporeal shock wave lithotripsy machine data, and additional features were created using Feature Creation. Finally, the impact of features on the models was analyzed using six methods to calculate feature importance. Our prediction model for the first lithotripsy, selected from among 43 methods and seven ensemble learning techniques, achieves an AUC of 0.91. For the second lithotripsy, the AUC reaches 0.76. The results indicate that the detailed and binary information provided by patients regarding their history of stone experiences contributes differently to the predictive accuracy of the first and second lithotripsy procedures. The prediction tool is available at https://predictor.isu.edu.tw/ks |
| 發表成果與本中心研究主題相關性 | 本研究成果與AI計畫的主題具有深度契合。透過運用先進的人工智能技術，成功構建了高效的預測模型，能夠精確評估體外震波碎石術的成功率。研究團隊從患者特徵、結石特徵以及醫療設備數據中提取並創建關鍵特徵，並以此為基礎，運用了43種方法和7種集成學習技術進行模型訓練，深入分析了各特徵對預測結果的重要性。 在應用這些AI技術後，模型在首次和第二次碎石術預測中的AUC值分別達到了0.91和0.76，展現出卓越的預測性能。這不僅彰顯了AI在醫療預測中的巨大潛力，更突出顯示了AI技術在提升醫療程序效能、優化資源配置和降低醫療成本方面的革命性作用。由此可見，該研究成果不僅在方法論上展現出極高的技術含量，還在應用層面與AI計畫的目標高度一致，進一步鞏固了AI在現代醫學領域中不可替代的地位。 |
