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首页> 外文期刊>JMIR Medical Informatics >Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation
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Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

机译：使用患者生命标志预测重症监护单位的住宿时间和死亡率：机器学习模型开发和验证

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Background Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.
机译：背景患者监测在所有护理阶段至关重要。特别是，重症监护单元（ICU）患者监测有可能降低并发症和发病率，并通过使医院提供更高质量，经济效益的患者护理，提高医疗服务质量，提高护理质量ICU。目的我们在此报告了ICU的逗留和死亡率预测模型的开发和验证。该模型将用于智能遥控患者监测（IRPM）框架的智能ICU患者监测模块，监测患者的健康状况，并在预测不良医疗条件时产生及时警报，机动指导或报告。方法我们利用公众提供的医疗信息MART进行重症监护（模拟）数据库，提取ICU的成人患者的住宿数据建立两种预测模型：一个用于死亡率预测，另一个用于ICU的逗留时间。对于死亡率模型，我们应用了六种常用的机器学习（ML）二进制分类算法，用于预测放电状态（存活或不存在）。对于保持模型的长度，我们使用22.64天的中位数患者人口ICU停留施加相同的六毫升算法进行二进制分类。对于基于回归的分类，我们使用了两个ML算法来预测天数。我们构建了两个预测模型的两个变体：一个使用12个基线人口统计和重要的标志特征，另一个基于我们所提出的量级方法，其中我们使用了从基线生命标志特征的21个额外​​的功能，包括它们的修改方式，标准偏差和量化百分比。结果我们可以使用量级方法保持合理性能的最小功能进行预测建模。使用随机林算法，死亡率模型中实现的最佳精度约为89％。基于群体中位数ICU（2.64天）的住院模型长度所达到的最高准确度约为65％，使用随机林算法约为65％。结论我们的方法中的新颖性是，我们建立了模型，以预测ICU的住宿和死亡率，具有合理的准确性，基于ML和量级方法的组合，这些方法仅利用患者配置文件的重要标志，无需使用任何外部功能。这种方法是基于所生命的标志的特征工程，包括其修改的手段，标准偏差和原始特征的定量百分比，这提供了更丰富的数据集来实现我们模型中更好的预测电力。

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