Medical project dynamic return rate intelligent evaluation method

By constructing a unified semantic event bus and analysis lake, and combining meta-learning and policy knowledge graphs, the problems of data drift and policy changes in the financial forecasting model of medical groups are solved, and dynamic rate of return assessment with second-level weight self-healing and transparent interpretation is achieved.

CN121258199BActive Publication Date: 2026-06-26GUANGZHOU LIYANGTAI MEDICAL MANAGEMENT CO LTD
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Patent Information

Application Number
CN202511443797.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-10-10
Publication Date
2026-06-26
Estimated Expiration
2045-10-10

AI Technical Summary

Technical Problem

Modern Medical Group's financial forecasting model is susceptible to data drift and policy changes, leading to the accumulation of forecasting errors and a lack of interpretability, making it difficult to maintain accuracy and transparency under high-frequency policy changes.

Method used

A unified semantic event bus and analysis lake are constructed. Drift vectors are output through dual-window drift detection to drive meta-learning fine-tuning. Combined with policy knowledge graph reasoning and reinforcement learning networks, an explanation matrix and a resource allocation matrix are generated to achieve second-level weight self-healing and real-time reflection of institutional changes.

Benefits of technology

It achieves the model's self-healing capability within seconds, ensuring that prediction results are synchronized with institutional changes, providing credible explanations, ensuring transparent decision-making, and dynamically balancing benefits and risks.

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Abstract

The application discloses a medical project dynamic return rate intelligent evaluation method, relates to the technical field of return rate evaluation, constructs a unified semantic event bus and analysis lake, and quickly crystallizes clinical, operation and financial flow into a return rate signal; double window drift detection outputs a drift vector to drive meta-learning fine tuning, realizes second-level weight self-healing; a policy knowledge graph reasoning generates a policy factor, a cash flow curve is injected through a differentiable adjustment function, and a yield curve synchronously reflects system changes; a hierarchical attention attribution is combined with entropy confidence evaluation to generate an explanation matrix and zero-knowledge traceability; a multi-channel splicing and graph convolution encode a state vector, a distributed reinforcement learning network outputs a resource allocation matrix, a reward function is embedded into tail risk and is continuously updated through a clipping proximal policy optimization; a safety fence checks a resource threshold and a red line clause, and forms an end-to-end collaborative system of data purification, model self-healing, policy agility, explanation transparency and scheduling closed loop.
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