Yang Xu, Ma Hongsheng, Gao Keyan, Ge Hui
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
The Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Life (Basel). 2022 Jul 28;12(8):1134. doi: 10.3390/life12081134.
It is of great significance to correctly infer the underlying cause of death for citizens, especially under the current worldwide situation. The medical resources of all countries are overwhelmed under the impact of coronavirus disease 2019 (COVID-19) and countries need to allocate limited resources to the most suitable place. Traditionally, the cause-of-death inference relies on manual methods, which require a large resource cost and are not so efficient. To address the challenges, in this work, we present a mixed inference method named Sink-CF. The Sink-CF algorithm is based on confidence measurement and is used to automatically infer the underlying cause of death of citizens. The method proposed in this paper combines a mathematical statistics method and a collaborative filtering and analysis algorithm in machine learning. Thus, our method can not only effectively achieve a certain accuracy, but also does not rely on a large quantity of manually labeled data to continuously optimize the model, which can save computer computing power and time, and has the characteristics of being simple, easy and efficient. The experimental results show that our method generates a reasonable precision (93.82%) and recall (90.11%) and outperforms other state-of-the-art machine learning algorithms.
正确推断公民的潜在死因具有重要意义，尤其是在当前全球形势下。在2019冠状病毒病（COVID-19）的冲击下，各国的医疗资源不堪重负，各国需要将有限的资源分配到最合适的地方。传统上，死因推断依赖手工方法，这需要大量资源成本且效率不高。为应对这些挑战，在这项工作中，我们提出了一种名为Sink-CF的混合推断方法。Sink-CF算法基于置信度度量，用于自动推断公民的潜在死因。本文提出的方法结合了数理统计方法和机器学习中的协同过滤与分析算法。因此，我们的方法不仅能有效达到一定的准确率，而且不依赖大量人工标注数据来持续优化模型，可节省计算机算力和时间，具有简单、易行、高效的特点。实验结果表明，我们的方法产生了合理的精确率（93.82%）和召回率（90.11%），并且优于其他先进的机器学习算法。
