Abstract

In today’s increasingly complex healthcare environment, China’s public hospitals face enormous financial challenges. The high degree of uncertainty and suddenness of financial risks make public hospitals need more sophisticated and real-time financial risk early warning mechanisms. To address this challenge, machine learning algorithms are introduced as a powerful tool to construct more accurate and efficient financial risk early warning models.the purpose of this dissertation is to summarize the recent research progress in constructing financial risk early warning models for Chinese public hospitals based on machine learning algorithms. The establishment of financial risk early warning models can not only help hospital management better understand the financial situation, but also identify potential risks in advance, which can provide powerful support for timely adjustment of strategies and countermeasures.

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