Method and system for building medical insurance hospitalization fee prediction model

Abstract

The invention discloses a method and a system for building a medical insurance hospitalization fee prediction model. Through a prediction model continuous optimization process, incremental data of social insurance data is regularly collected and stored in a training database; and according to the incremental data, a model prediction effect of a prediction model of a previous version is checked, anobtained model prediction result is compared with a real value, whether a model training task is re-triggered and started or not is judged according to a comparison value, sample data after trainingand updating is optimized through a machine learning algorithm, and a prediction model with a better effect is built and stored. Based on continuous new data, the prediction effect is tracked, continuous optimization is performed, and an optimal model building system is realized.

Description

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AI Technical Summary

Benefits of technology

This invention describes systems used by healthcare providers (such as hospitals or clinics). They use their own data sources like Social Insure Business Data from different parts of the world to create predictive models with high accuracy. These predictions are then trained into these models to provide accurate advice about how long it takes careers until they get done well enough for them to take home safely after treatment. Overall, this technology helps reduce costs associated with patient waiting times while ensuring proper management decisions can be made.

Problems solved by technology

The technical problem addressed by this patented research relates to improving healthcare costs while reducing their impact on patients' financial situation during recovery periods due to poorly understood or overlooked factors like economic policies and regulations related thereto. This includes identifying potential sources of income loss from unhealthy individuals who require care after being treated for illness. Current solutions involve analyzing these types of claims made against existing risk measures (such as traditional prevention) rather than making informed decisions about how much money will pay off if those benefits exceed what they were worth before it was done.

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