Prognostic risk prediction model construction system and method based on chronic disease real-world data

By constructing a prognostic risk prediction model system based on real-world data of chronic diseases, the problems of low data quality and lack of consideration of comorbidity characteristics in chronic liver disease, diabetes, and hypertension have been solved. This has enabled high-quality data processing and individualized risk quantification, improving the clinical adaptability and translational efficiency of the model.

CN122337671APending Publication Date: 2026-07-03HEFEI ZESHENXIN MEDICAL TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610568536.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-28
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

In existing technologies, there is a lack of standardized solutions for processing real-world data on chronic liver disease, diabetes, and hypertension. The quality of multi-source heterogeneous data is low, the models do not consider comorbidity features, the prediction results have poor clinical adaptability, the model training process is not optimized for bias, and there is a lack of individualized risk quantification and clinical interpretation, resulting in low efficiency of the models in clinical applications.

Method used

A prognostic risk prediction model system based on real-world data of chronic diseases is constructed, including multi-source data collection and access, data standardization and quality control, multi-dimensional feature engineering and screening, comorbidity feature fusion, prognostic risk prediction model training and optimization, multi-dimensional model validation and evaluation, deployment and risk stratification output. It adopts clinical-data dual-dimensional feature screening, multi-model fusion and hyperparameter optimization, and combines quantitative formulas to realize individualized risk calculation.

Benefits of technology

It improved data quality, enhanced the clinical adaptability and robustness of the model, achieved individualized risk quantification and interpretable prediction results, improved the translation efficiency of the model from the laboratory to the clinic, and supported the development of individualized intervention programs.

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Abstract

The invention discloses a prognosis risk prediction model construction system and method based on chronic disease real world data. Comprising a multi-source real world data acquisition and access module, a data standardization and quality control module, a multi-dimensional feature engineering and screening module, a common disease feature fusion and data set construction module, a prognosis risk prediction model training and optimization module, a model multi-dimensional verification and evaluation module and a model deployment and risk hierarchical output module. A special multi-source real-world data acquisition and quality control system for chronic liver diseases, diabetes and hypertension is constructed, data standardization processing is realized in combination with a chronic disease clinical diagnosis and treatment guide, and the quality and availability of real-world data are effectively improved through multi-dimensional missing value and abnormal value processing and data de-duplication integration, so that the real-world data acquisition and quality control system is suitable for clinical diagnosis and treatment of chronic diseases. The core problems of disorder and low quality of real world data in the prior art are solved, and a high-quality data source basis is provided for model construction.
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