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首页> 外文会议>MEDINFO >Development of a Machine Learning Model Predicting an ICU Admission for Patients with Elective Surgery and Its Prospective Validation in Clinical Practice
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Development of a Machine Learning Model Predicting an ICU Admission for Patients with Elective Surgery and Its Prospective Validation in Clinical Practice

机译：开发机器学习模型，预测选修外科患者ICU入院及临床实践中的前瞻性验证

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Frequent utilization of the Intensive Care Unit (ICU) is associated with higher costs and decreased availability for patients who urgently need it. Common risk assessment tool, like the ASA score, lack objectivity and do account only for some influencing parameters. The aim of our study was (1) to develop a reliable machine learning model predicting ICU admission risk after elective surgery, and (2) to implement it in a clinical workflow. We used electronic medical records from more than 61,000 patients for modelling. A random forest model outperformed other methods with an area under the curve of 0.91 in the retrospective test set. In the prospective implementation, the model achieved a sensitivity of 73.3% and a specificity of 80.8%. Further research is essential to determine physicians’ attitudes to machine learning models and assess the long term improvement of ICU management.
机译：频繁利用重症监护单元（ICU）与较高的成本和迫切需要它的患者的可用性降低有关。 常见风险评估工具，如ASA得分，缺乏客观性，仅用于一些影响参数。 我们的研究目的是（1）开发一种可靠的机器学习模型，预测ICU入学风险在选修外科，（2）在临床工作流程中实施它。 我们使用超过61,000名患者的电子医疗记录进行建模。 随机森林模型在回顾试验组中呈0.91的曲线下的其他方法表现优于其他方法。 在前瞻性实施中，该模型达到了73.3％的敏感性，特异性为80.8％。 进一步的研究对于确定医生对机器学习模型的态度并评估ICU管理的长期改进至关重要。

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