Galicia Sur Health Research Institute (IIS Galicia Sur), Álvaro Cunqueiro Hospital, 36310 Vigo, Spain.
Department of Electronic Technology, University of Vigo, 36310 Vigo, Spain.
Int J Environ Res Public Health. 2023 Feb 16;20(4):3455. doi: 10.3390/ijerph20043455.
It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today's hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the ICU in the same hospital stay have a higher risk of mortality, morbidity, longer length of stay, and increased cost. The methodology proposed to predict ICU readmission could improve the patients' care. The objective of this work is to explore and evaluate the potential improvement of existing models for predicting early ICU patient readmission by using optimized artificial intelligence algorithms and explainability techniques. In this work, XGBoost is used as a predictor model, combined with Bayesian techniques to optimize it. The results obtained predicted early ICU readmission (AUROC of 0.92 ± 0.03) improves state-of-the-art consulted works (whose AUROC oscillate between 0.66 and 0.78). Moreover, we explain the internal functioning of the model by using Shapley Additive Explanation-based techniques, allowing us to understand the model internal performance and to obtain useful information, as patient-specific information, the thresholds from which a feature begins to be critical for a certain group of patients, and the feature importance ranking.
开发并引入新技术，使用最先进的人工智能方法，自动有效地分析当今医院产生的大量数据，这是非常有意义的。在同一住院期间再次入住 ICU 的患者的死亡率、发病率更高，住院时间更长，成本增加。提出的预测 ICU 再入院的方法可以改善患者的护理。本工作的目的是探索和评估通过使用优化的人工智能算法和可解释性技术，对现有预测早期 ICU 患者再入院的模型进行潜在改进。在本工作中，使用 XGBoost 作为预测模型，并结合贝叶斯技术对其进行优化。所获得的结果预测了早期 ICU 再入院（AUROC 为 0.92 ± 0.03），优于已咨询的最先进技术（其 AUROC 在 0.66 到 0.78 之间波动）。此外，我们使用 Shapley Additive Explanation-based 技术来解释模型的内部功能，使我们能够了解模型的内部性能并获得有用的信息，例如患者特定信息、特征开始对某些患者群体变得关键的阈值，以及特征重要性排序。
