Sun Chenxi, Li Hongyan, Song Moxian, Cai Derun, Zhang Baofeng, Hong Shenda
Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing 100871, China.
School of Intelligence Science and Technology, Peking University, Beijing 100871, China.
Patterns (N Y). 2023 Feb 3;4(2):100687. doi: 10.1016/j.patter.2023.100687. eCollection 2023 Feb 10.
Continuous diagnosis and prognosis are essential for critical patients. They can provide more opportunities for timely treatment and rational allocation. Although deep-learning techniques have demonstrated superiority in many medical tasks, they frequently forget, overfit, and produce results too late when performing continuous diagnosis and prognosis. In this work, we summarize the four requirements; propose a concept, continuous classification of time series (CCTS); and design a training method for deep learning, restricted update strategy (RU). The RU outperforms all baselines and achieves average accuracies of 90%, 97%, and 85% on continuous sepsis prognosis, COVID-19 mortality prediction, and eight disease classifications, respectively. The RU can also endow deep learning with interpretability, exploring disease mechanisms through staging and biomarker discovery. We find four sepsis stages, three COVID-19 stages, and their respective biomarkers. Further, our approach is data and model agnostic. It can be applied to other diseases and even in other fields.
连续诊断和预后对重症患者至关重要。它们可以为及时治疗和合理分配提供更多机会。尽管深度学习技术在许多医疗任务中已显示出优势，但在执行连续诊断和预后时，它们经常出现遗忘、过拟合的问题，并且产生结果的时间过晚。在这项工作中，我们总结了四个要求；提出了一个概念，即时间序列连续分类（CCTS）；并设计了一种深度学习训练方法，即受限更新策略（RU）。RU在所有基线方法上均表现出色，在连续脓毒症预后、COVID-19死亡率预测和八种疾病分类上分别达到了90%、97%和85%的平均准确率。RU还可以赋予深度学习可解释性，通过分期和生物标志物发现来探索疾病机制。我们发现了四个脓毒症阶段、三个COVID-19阶段及其各自的生物标志物。此外，我们的方法与数据和模型无关。它可以应用于其他疾病，甚至其他领域。
