Data processing model training method and device and data processing method and device

By calculating target calibration weights and training a calibration estimator, the selection bias problem caused by censoring data in survival analysis is solved, improving the generalization performance and estimation accuracy of the data processing model.

CN121614872APending Publication Date: 2026-03-06ZHEJIANG E COMMERCE BANK CO LTD +1
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Patent Information

Application Number
CN202511833884.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-05
Publication Date
2026-03-06

AI Technical Summary

Technical Problem

In survival analysis, censored data due to experimental window limitations can lead to selection bias if uncensored data is used directly for analysis, resulting in inaccurate analysis results.

Method used

By determining the conditional probabilities of the first and second experimental data, the target calibration weights are calculated, the calibration estimator is trained, and the estimation results are calibrated using the calibration weights to obtain the calibration results. The initial data processing model is then trained to correct the selection bias.

Benefits of technology

It significantly improves the generalization performance and estimation accuracy of the target data processing model in the presence of censored data, making its output closer to the unbiased estimate of the true population distribution.

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Abstract

Embodiments of the invention provide a data processing model training method and apparatus, and a data processing method and apparatus. The data processing model training method comprises the steps of determining a first conditional probability corresponding to first experimental data and a second conditional probability corresponding to second experimental data; training according to a target calibration weight calculated according to the first conditional probability and the second conditional probability to obtain a calibration estimator; inputting the target confusion data and the corresponding observation result into a calibration estimator to obtain a first calibration weight, and inputting the target confusion data and the estimation result into the calibration estimator to obtain a second calibration weight; processing the estimation result according to the two calibration weights to obtain a calibration result, inputting the target confusion data and the target intervention data into an initial data processing model to obtain a prediction result, and training according to the prediction result and the calibration result to obtain a target data processing model; the problem of selection deviation in survival analysis is solved by calibrating the estimator and adjusting the weight.
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