Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance
Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance
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DOI：
10.1093/neuros/nyy343
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发表时间：
2019-09-01
期刊：
影响因子：
4.8
通讯作者：
Chambless, Lola B.
中科院分区：
文献类型：
--
作者：
Muhlestein, Whitney E.;Akagi, Dallin S.;Chambless, Lola B.
目前的结果预测工具主要基于回归方法并受到回归方法的限制。利用可以处理多种不同输入的机器学习（ML）方法可以增强预测能力并改善患者的预后。住院时间（LOS）是这样一个结果，作为一个替代病人的疾病严重程度和资源utilization. ObjectiveTo开发一种新的方法，系统地排名，选择，并联合收割机ML算法建立一个模型，预测LOS开颅手术后脑tumor.METHODS的训练数据集的41222例患者接受开颅手术的脑肿瘤是从全国住院样本。在26个术前变量上训练了29个ML算法来预测LOS。通过计算均方根对数误差（RMSLE）对训练算法进行排名，并将表现最好的算法组合起来形成一个集成。使用来自国家手术质量改进计划的4592名患者的数据集对该集成进行了外部验证。额外的分析确定的变量，最强烈地影响合奏模型predictions.RESULTS合奏模型预测LOS与RMSLE的.555（95%置信区间，.553-.557）的内部验证和.631的外部验证。非选择性手术，术前肺炎，钠异常，或体重减轻，和非白人种族是最强的预测增加LOS.CONCLUSION ML合奏模型预测LOS具有良好的性能，内部和外部验证，并产生临床的见解，可能会改善患者的预后。这种系统的ML方法可以应用于广泛的临床问题，以改善患者护理。
BACKGROUND Current outcomes prediction tools are largely based on and limited by regression methods. Utilization of machine learning (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outcomes. Inpatient length of stay (LOS) is one such outcome that serves as a surrogate for patient disease severity and resource utilization.OBJECTIVE To develop a novel method to systematically rank, select, and combine ML algorithms to build a model that predicts LOS following craniotomy for brain tumor.METHODS A training dataset of 41222 patients who underwent craniotomy for brain tumor was created from the National Inpatient Sample. Twenty-nine ML algorithms were trained on 26 preoperative variables to predict LOS. Trained algorithms were ranked by calculating the root mean square logarithmic error (RMSLE) and top performing algorithms combined to form an ensemble. The ensemble was externally validated using a dataset of 4592 patients from the National Surgical Quality Improvement Program. Additional analyses identified variables that most strongly influence the ensemble model predictions.RESULTS The ensemble model predicted LOS with RMSLE of .555 (95% confidence interval, .553-.557) on internal validation and .631 on external validation. Nonelective surgery, preoperative pneumonia, sodium abnormality, or weight loss, and non-White race were the strongest predictors of increased LOS.CONCLUSION An ML ensemble model predicts LOS with good performance on internal and external validation, and yields clinical insights that may potentially improve patient outcomes. This systematic ML method can be applied to a broad range of clinical problems to improve patient care.