论文标题
完美预测ICU的住宿时间：太好了，无法实现
Perfectly predicting ICU length of stay: too good to be true
论文作者
论文摘要
Alsinglawi等人的一篇论文最近被接受并发表在科学报告中。在本文中，作者的目的是使用各种机器学习技术来预测ICU部门中肺癌患者的长期（> 7天）或短期（<7天）的长期（> 7天）。作者声称，在接收器操作特征曲线（AUROC）下的区域（AUROC）下，具有随机的森林（RF）分类器，并具有ADASYN类平衡对采样技术，如果准确的情况可能对医院的管理产生重大影响。但是，我们已经确定了手稿中的几个方法论缺陷，这些缺陷导致结果过于乐观，如果在临床实践中使用，将会产生严重的后果。此外，该方法的报告尚不清楚，手稿中缺少许多重要的细节，这使得繁殖极为困难。我们强调了这些疏忽对结果的影响，并在纠正这些疏忽时提供了88.91％AUROC的结果。
A paper of Alsinglawi et al was recently accepted and published in Scientific Reports. In this paper, the authors aim to predict length of stay (LOS), discretized into either long (> 7 days) or short stays (< 7 days), of lung cancer patients in an ICU department using various machine learning techniques. The authors claim to achieve perfect results with an Area Under the Receiver Operating Characteristic curve (AUROC) of 100% with a Random Forest (RF) classifier with ADASYN class balancing over sampling technique, which if accurate could have significant implications for hospital management. However, we have identified several methodological flaws within the manuscript which cause the results to be overly optimistic and would have serious consequences if used in a clinical practice. Moreover, the reporting of the methodology is unclear and many important details are missing from the manuscript, which makes reproduction extremely difficult. We highlight the effect these oversights have had on the result and provide a more believable result of 88.91% AUROC when these oversights are corrected.
