Sepsis is a deadly condition affecting many patients in the hospital. Recent studies have shown that patients diagnosed with sepsis have significant mortality and morbidity, resulting from the body's dysfunctional host response to infection. Clinicians often rely on the use of Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and the Modified Early Warning Score (MEWS) to identify early signs of clinical deterioration requiring further work-up and treatment. However, many of these tools are manually computed and were not designed for automated computation. There have been different methods used for developing sepsis onset models, but many of these models must be trained on a sufficient number of patient observations in order to form accurate sepsis predictions. Additionally, the accurate annotation of patients with sepsis is a major ongoing challenge. In this paper, we propose the use of Active Learning Recurrent Neural Networks (ALRts) for short temporal horizons to improve the prediction of irregularly sampled temporal events such as sepsis. We show that an active learning RNN model trained on limited data can form robust sepsis predictions comparable to models using the entire training dataset.
翻译:最近的研究表明,经诊断患有败血症的病人由于身体主机对感染的反应功能失调而导致的死亡率和发病率很高;临床医生往往依赖使用序列器官衰竭评估、系统炎反应综合症(SIRS)和修改后的预警评分,以确认临床恶化的早期迹象,需要进一步的检查和治疗;然而,许多这些工具都是人工计算,并且不是设计用于自动计算;在开发败血症发病模型时使用了不同的方法,但其中许多模型必须经过足够数量的病人观察培训,以便作出准确的败血预测;此外,对患有败血症的病人进行准确的注解是一个持续的主要挑战;在本文件中,我们提议使用活跃的常态神经网络(ALRTs)来短期利用短期学习来改进诸如败血症等不定期抽样的时空事件的预测;我们表明,积极学习的受有限数据训练的RNNN模型可以形成可靠的败血症预测,从而形成与使用整个培训数据模型的可靠。