Mechanism Analysis of High-Quality Development of Medical Insurance Supervision in the Context of Intelligent Management of Medical Record Information
- DOI
- 10.2991/978-94-6463-946-9_27How to use a DOI?
- Keywords
- Deep Learning; DIP; Medical Insurance
- Abstract
China’s medical insurance supervision is facing new challenges, with hidden and complex medical violations leading to waste of funds, damage to patient interests, and misallocation of resources. The popularity of electronic medical systems has led to the storage of a large amount of medical information in the form of electronic medical records. However, the reliance of traditional natural language processing techniques on unlabeled data limits their effective application. Deep learning algorithms can effectively process unlabeled data through unsupervised learning, solving the problems of information dispersion and low utilization. Moreover, the intelligent medical insurance supervision technology based on deep learning improves the efficiency and safety of medical services through abnormal data detection and disease score payment.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ying Han AU - Chengyi Pu AU - Wenjing Fan AU - Hui Li AU - Xiaoli Song PY - 2026 DA - 2026/01/04 TI - Mechanism Analysis of High-Quality Development of Medical Insurance Supervision in the Context of Intelligent Management of Medical Record Information BT - Proceedings of the 11th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2024) PB - Atlantis Press SP - 212 EP - 218 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-946-9_27 DO - 10.2991/978-94-6463-946-9_27 ID - Han2026 ER -