Intelligent processing method and system for clinical research data
By analyzing the correlation and influence between data subsets and reference subsets in a multidimensional index dataset, and adaptively adjusting clustering parameters, the problem of low accuracy in identifying outlier data caused by improper selection of clustering parameters in existing technologies is solved, achieving higher identification accuracy and analysis reliability.
Patent Information
- Application Number
- CN202511826614.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-05
- Publication Date
- 2026-06-09
- Estimated Expiration
- 2045-12-05
AI Technical Summary
Existing methods for identifying abnormal data in clinical research data often suffer from low accuracy due to improper selection of clustering parameters, making it difficult to effectively distinguish between individual patient differences and genuinely erroneous data.
By obtaining the degree of correlation and influence between each data subset and the reference subset in the multidimensional indicator dataset, the clustering parameters are adaptively adjusted, and methods such as principal component analysis, scatter plot fitting, and DBSCAN clustering are used to identify outlier data.
It improved the accuracy of abnormal data identification, reduced the impact of individual patient differences on the results, and enhanced the reliability of the analysis results.
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Abstract
Citation Information
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