Considering the efficiency and security of healthcare data processing, indiscriminate data collection, annotation, and transmission are unwise. In this article, we propose the normalized double entropy (NDE) method to assess image data quality in the form of metatask. In specific, the probability entropy and distance entropy are both adopted and normalized to evaluate the data quality. The experimental results show the stable ability of the NDE to distinguish good and bad data in terms of information contribution. Furthermore, the model's diagnostic performances driven by selected good and bad data are compared, and a clear gap exists between them under the premise of the same amount of data. Screening 70% of the dataset can achieve almost the same accuracy as that based on all data. This article focuses on healthcare data quality and data redundancy and provides a practical evaluation tool to facilitate the identification and collection of valuable data, which is beneficial to improve efficiency and protect cybersecurity in healthcare systems.

中文翻译：

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网络安全情报的医疗保健数据质量评估

考虑到医疗数据处理的效率和安全性，不加选择的数据收集、注释和传输是不明智的。在本文中，我们提出了归一化双熵（NDE）方法，以元任务的形式评估图像数据质量。具体而言，概率熵和距离熵均被采用和归一化来评估数据质量。实验结果表明 NDE 在信息贡献方面具有稳定的区分好坏数据的能力。此外，比较了选择好的和坏数据驱动的模型诊断性能，在数据量相同的前提下，两者之间存在明显的差距。筛选 70% 的数据集可以达到与基于所有数据的几乎相同的准确度。