Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Science, No. 4, Rashid Yasemi Street, Vali-e Asr Avenue, Tehran, 1996713883, Iran.
Antimicrobial Resistance Research Center, Communicable Disease Institute, Mazandaran University of Medical Sciences, Sari, Iran.
BMC Med Inform Decis Mak. 2022 Jun 26;22(1):167. doi: 10.1186/s12911-022-01903-9.
A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared with artificial intelligence predictive models (Artificial Neural Network and Decision Tree) in terms of the prediction of the survival rate of the patients admitted to the intensive care unit.
This retrospective cohort study was performed on the data of the patients admitted to the ICU of Ghaemshahr's Razi Teaching Care Center from March 20th, 2017, to September 22nd, 2019. The required data for calculating conventional severity classification models (SOFA, SAPS II, APACHE II, and APACHE IV) were collected from the patients' medical records. Subsequently, the score of each model was calculated. Artificial intelligence predictive models (Artificial Neural Network and Decision Tree) were developed in the next step. Lastly, the performance of each model in predicting the survival of the patients admitted to the intensive care unit was evaluated using the criteria of sensitivity, specificity, accuracy, F-measure, and area under the ROC curve. Also, each model was validated externally. The R program, version 4.1, was used to create the artificial intelligence models, and SPSS Statistics Software, version 21, was utilized to perform statistical analysis.
The area under the ROC curve of SOFA, SAPS II, APACHE II, APACHE IV, multilayer perceptron artificial neural network, and CART decision tree were 76.0, 77.1, 80.3, 78.5, 84.1, and 80.0, respectively.
The results showed that although the APACHE II model had better results than other conventional models in predicting the survival rate of the patients admitted to the intensive care unit, the other conventional models provided acceptable results too. Moreover, the findings showed that the artificial neural network model had the best performance among all the studied models, indicating the discrimination power of this model in predicting patient survival compared to the other models.
疾病严重程度分类系统广泛用于预测不同诊断的 ICU 患者的存活率。在本研究中，比较了传统严重程度分类系统与人工智能预测模型（人工神经网络和决策树）在预测 ICU 患者存活率方面的表现。
本回顾性队列研究对 2017 年 3 月 20 日至 2019 年 9 月 22 日期间入住 Ghaemshahr 的 Razi 教学护理中心 ICU 的患者数据进行了研究。从患者病历中收集了计算传统严重程度分类模型（SOFA、SAPS II、APACHE II 和 APACHE IV）所需的数据。随后，计算了每个模型的评分。在下一步中，开发了人工智能预测模型（人工神经网络和决策树）。最后，使用灵敏度、特异性、准确性、F 度量和 ROC 曲线下面积等标准评估了每个模型预测 ICU 患者存活率的性能。此外，还对每个模型进行了外部验证。使用 R 程序，版本 4.1，创建人工智能模型，使用 SPSS Statistics 软件，版本 21，进行统计分析。
SOFA、SAPS II、APACHE II、APACHE IV、多层感知器人工神经网络和 CART 决策树的 ROC 曲线下面积分别为 76.0、77.1、80.3、78.5、84.1 和 80.0。
结果表明，尽管 APACHE II 模型在预测 ICU 患者存活率方面的结果优于其他传统模型，但其他传统模型的结果也可以接受。此外，研究结果表明，人工神经网络模型在所有研究模型中表现最好，表明该模型在预测患者生存方面的区分能力优于其他模型。
