Healthcare Data Quality Assessment for Cybersecurity Intelligence

Abstract

Considering the efficiency and security of healthcare data processing, indiscriminate data collection, annotation, and transmission are unwise. In this work, we propose the normalized double entropy (NDE) method to assess image data quality in the form of meta-task. 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. Further, 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 work 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.