Pappada S, Sathelly B, Schmieder J, Javaid A, Owais M, Cameron B, Khuder S, Kostopanagiotou G, Smith R, Sparkle T, Papadimos T
Department of Anesthesiology, College of Medicine and Life Sciences, University of Toledo, Toledo, Ohio, USA.
Department of Bioengineering, University of Toledo, Toledo, Ohio, USA.
Hippokratia. 2024 Jan-Mar;28(1):1-10.
Detecting liver dysfunction/failure in the intensive care unit poses a challenge as individuals afflicted with these conditions often appear symptom-free, thereby complicating early diagnoses and contributing to unfavorable patient outcomes. The objective of this endeavor was to improve the chances of early diagnosis of liver dysfunction/failure by creating a predictive model for the critical care setting. This model has been designed to produce an index that reflects the probability of severe liver dysfunction/failure for patients in intensive care units, utilizing machine learning techniques.
This effort used comprehensive open-access patient databases to build and validate machine learning-based models for predicting the likelihood of severe liver dysfunction/failure. Two artificial neural network model architectures that derived a novel 0-100 Liver Failure Risk Index were developed and validated using the comprehensive patient databases. Data used to train and develop the models included clinical (patient vital signs) and laboratory results related to liver function which included liver function test results. The performance of the developed models was compared in terms of sensitivity, specificity, and the mean lead time to diagnosis.
The best model performance demonstrated an 83.3 % sensitivity and a specificity of 77.5 % in diagnosing severe liver dysfunction/failure. This model accurately identified these patients a median of 17.5 hours before their clinical diagnosis, as documented in their electronic health records. The predictive diagnostic capability of the developed models is crucial to the intensive care unit setting, where treatment and preventative interventions can be made to avoid severe liver dysfunction/failure.
Our machine learning approach facilitates early and timely intervention in the hepatic function of critically ill patients by their healthcare providers to prevent or minimize associated morbidity and mortality. HIPPOKRATIA 2024, 28 (1):1-10.
在重症监护病房中检测肝功能障碍/衰竭是一项挑战，因为患有这些病症的患者通常没有症状，这使得早期诊断变得复杂，并导致患者预后不良。这项工作的目的是通过创建一个针对重症监护环境的预测模型，提高肝功能障碍/衰竭早期诊断的几率。该模型旨在利用机器学习技术生成一个反映重症监护病房患者发生严重肝功能障碍/衰竭概率的指数。
这项工作使用了全面的开放获取患者数据库，以构建和验证基于机器学习的预测严重肝功能障碍/衰竭可能性的模型。开发并使用综合患者数据库验证了两种人工神经网络模型架构，它们得出了一个新的0至100的肝衰竭风险指数。用于训练和开发模型的数据包括临床数据（患者生命体征）和与肝功能相关的实验室结果，其中包括肝功能测试结果。从敏感性、特异性和平均诊断提前时间方面对所开发模型的性能进行了比较。
最佳模型性能在诊断严重肝功能障碍/衰竭时显示出83.3%的敏感性和77.5%的特异性。如电子健康记录所示，该模型在临床诊断前中位数17.5小时准确识别出这些患者。所开发模型的预测诊断能力对于重症监护病房环境至关重要，在该环境中可以进行治疗和预防干预以避免严重肝功能障碍/衰竭。
我们的机器学习方法有助于医疗保健提供者对重症患者的肝功能进行早期和及时干预，以预防或尽量减少相关的发病率和死亡率。《希波克拉底》2024年，28(1):1 - 10。
