Universidade Federal de Minas Gerais, Brazil; Universidade Estadual de Feira de Santana, Brazil.
Universidade Federal de Minas Gerais, Brazil; Lifecenter Hospital, Brazil.
Artif Intell Med. 2022 Jun;128:102283. doi: 10.1016/j.artmed.2022.102283. Epub 2022 Mar 22.
The aim of this study is to build machine learning models to predict severe complications using administrative and clinical elements that are collected immediately after patient admission to the intensive care unit (ICU). Risk models are of increasing importance in the ICU setting. However, they generally present the black-box issue because they do not provide meaningful information about the logic involved in patient-specific predictions. Fortunately, effective algorithms exist for explaining black-box models, and in practice, they offer valuable explanations for model predictions. These explanations are becoming essential to engender trust and accreditation to the model. However, once the model is implemented, a major issue is whether it will continue to employ the same prediction logic as originally intended to. To build our models, features are obtained from patient administrative data, laboratory results and vital signs available within the first hour after ICU admission. This enables our models to provide great anticipation because complications can occur at any moment during ICU stay. To build models that continue to work as originally designed we first propose to measure (i) how the provided explanations vary for different inputs (that is, robustness), and (ii) how the provided explanations change with models built from different patient sub-populations (that is, stability). Second, we employ these measures as regularization terms that are coupled with a feature selection procedure such that the final model provides predictions with more robust and stable explanations. Experiments were conducted on a dataset containing 6000 ICU admissions of 5474 patients. Results obtained on an external validation cohort of 1069 patients with 1086 ICU admissions showed that selecting features based on robustness led to gains in terms of predictive power that varied from 6.8% to 9.4%, whereas selecting features based on stability led to gains that varied from 7.2% to 11.5%, depending on the target complication. Our results are of practical importance as our models predict complications with great anticipation, thus facilitating timely and protective interventions.
本研究旨在构建使用患者入住重症监护病房（ICU）后立即采集的管理和临床数据来预测严重并发症的机器学习模型。风险模型在 ICU 环境中越来越重要。然而，它们通常存在“黑箱”问题，因为它们没有提供关于患者特定预测所涉及的逻辑的有意义信息。幸运的是，现在存在有效的算法可以解释黑箱模型，并且在实践中，它们为模型预测提供了有价值的解释。这些解释对于建立模型的可信度和认证至关重要。然而，一旦模型被实施，一个主要问题是它是否会继续采用最初预期的相同预测逻辑。为了构建我们的模型，从患者的管理数据、实验室结果和 ICU 入住后第一个小时内的生命体征中获取特征。这使我们的模型能够提供很好的预测，因为并发症可能在 ICU 住院期间的任何时候发生。为了构建继续按最初设计工作的模型，我们首先提出测量：（i）对于不同的输入，提供的解释如何变化（即稳健性）；（ii）对于从不同患者子群体构建的模型，提供的解释如何变化（即稳定性）。其次，我们将这些措施用作正则化项，与特征选择过程相结合，使得最终模型提供的预测具有更稳健和稳定的解释。我们在一个包含 6000 例 5474 名患者 ICU 入院的数据集上进行了实验。在包含 1069 例 1086 例 ICU 入院的外部验证队列上的实验结果表明，基于稳健性选择特征可在预测能力方面带来 6.8%至 9.4%的增益，而基于稳定性选择特征可带来 7.2%至 11.5%的增益，这取决于目标并发症。我们的结果具有实际意义，因为我们的模型可以提前预测并发症，从而便于及时采取保护措施。
