Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43Gil, Songpagu, Seoul, 05505, Republic of Korea.
Department of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Sonpagu, 05505, Seoul, Republic of Korea.
BMC Med Inform Decis Mak. 2024 Nov 20;24(1):350. doi: 10.1186/s12911-024-02755-1.
Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes.
Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting from the length of stay prediction.
A retrospective cohort study was conducted from January 2018 to October 2020. A total cohort of 240,000 patients' medical records was collected. The data were collected exclusively for preoperative variables to accurately analyze the predictive factors impacting the postoperative length of stay. The main outcome of this study is an analysis of the length of stay (in days) after surgery until discharge. The prediction was performed with ridge regression, random forest, XGBoost, and multi-layer perceptron neural network models.
The XGBoost resulted in the best performance with an average error within 3 days. Moreover, we explain each feature's contribution over the XGBoost model and further display distinct predictors affecting the overall prediction outcome at the patient level. The risk factors that most importantly contributed to the stay after surgery were as follows: a direct bilirubin laboratory test, department change, calcium chloride medication, gender, and diagnosis with the removal of other organs. Our results suggest that healthcare providers take into account the risk factors such as the laboratory blood test, distributing patients, and the medication prescribed prior to the surgery.
We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.
提前预测住院时间不仅对医院的临床和财务有利，还能使医疗保健提供者更好地做出决策，提高护理质量。更重要的是，了解需要全身麻醉的重症患者的住院时间是提高健康结果的关键。
在这里，我们旨在发现机器学习如何支持资源分配管理和决策，从而预测住院时间。
进行了一项回顾性队列研究，时间为 2018 年 1 月至 2020 年 10 月。共收集了 24 万例患者的病历数据。这些数据仅用于术前变量，以准确分析影响术后住院时间的预测因素。本研究的主要结果是分析手术后直至出院的住院时间（以天为单位）。使用岭回归、随机森林、XGBoost 和多层感知机神经网络模型进行预测。
XGBoost 的表现最好，平均误差在 3 天以内。此外，我们解释了每个特征在 XGBoost 模型中的贡献，并进一步展示了影响总体预测结果的不同预测因素在患者水平上的表现。对手术后住院时间最重要的贡献的风险因素如下：直接胆红素实验室检测、科室变动、氯化钙药物、性别和其他器官切除的诊断。我们的研究结果表明，医疗保健提供者应考虑实验室血液检查、患者分配和手术前开的药物等风险因素。
我们成功地预测了手术后的住院时间，并提供了可解释的模型和支持性分析。总的来说，我们通过 XGBoost 模型展示了对术前特征的解释，并确定了与住院时间结果相关的更高风险预测因素，为未来电子病历的住院时间预测提供了深入的知识支持，有助于决策和手术部门的顺利进行。
