Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes
Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes
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DOI：
10.1089/end.2018.0035
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发表时间：
2018-05-01
影响因子：
2.7
通讯作者：
Liu, Yan
中科院分区：
文献类型：
--
作者：
Hung, Andrew J.;Chen, Jian;Liu, Yan
目的：手术表现对临床结果至关重要。我们提出了一种新的机器学习（ML）方法来处理自动性能指标（APMs），以评估手术性能并预测机器人辅助根治性前列腺切除术（RARP）后的临床结果。材料和方法：我们直接从78例RARP病例的机器人系统数据（训练材料）和住院时间（LOS；训练标签）（2天）中使用APMs训练了三种ML算法，并选择了性能最好的算法。所选算法将案例分类为“预测为预期LOS （pExp-LOS）”和“预测为扩展LOS （pExt-LOS）”。我们比较两组的术后结果（Kruskal-Wallis/Fisher精确试验）。然后，该算法预测个人临床结果，我们将其与实际结果（Spearman的相关性/Fisher的精确测试）进行比较。最后，我们确定了算法在预测过程中采用的五个最相关的apm。结果：随机森林-50 （Random Forest-50, RF-50）算法的LOS预测准确率最高，达到87.2%（“pExp-LOS”73例，“pExt-LOS”5例）。“pExp-LOS”组在手术时间（3.7小时vs 4.6小时，p=0.007）、LOS（2天vs 4天，p=0.02）和Foley持续时间（9天vs 14天，p=0.02）方面优于“pExt-LOS”组。算法预测的患者预后与手术时间的基础真实值有显著相关性(p
Purpose: Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP).Materials and Methods: We trained three ML algorithms utilizing APMs directly from robot system data (training material) and hospital length of stay (LOS; training label) (2 days) from 78 RARP cases, and selected the algorithm with the best performance. The selected algorithm categorized the cases as "Predicted as expected LOS (pExp-LOS)" and "Predicted as extended LOS (pExt-LOS)." We compared postoperative outcomes of the two groups (Kruskal-Wallis/Fisher's exact tests). The algorithm then predicted individual clinical outcomes, which we compared with actual outcomes (Spearman's correlation/Fisher's exact tests). Finally, we identified five most relevant APMs adopted by the algorithm during predicting.Results: The "Random Forest-50" (RF-50) algorithm had the best performance, reaching 87.2% accuracy in predicting LOS (73 cases as "pExp-LOS" and 5 cases as "pExt-LOS"). The "pExp-LOS" cases outperformed the "pExt-LOS" cases in surgery time (3.7 hours vs 4.6 hours, p=0.007), LOS (2 days vs 4 days, p=0.02), and Foley duration (9 days vs 14 days, p=0.02). Patient outcomes predicted by the algorithm had significant association with the ground truth in surgery time (p