GAN Imputation Model for Missing Medical Diagnosis Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for imputing missing medical diagnosis data are complex and inefficient, limiting their ability to effectively process and improve the integrity of medical data analysis.
Innovation Solution
A method using a generative adversarial network (GAN) imputation model that involves randomly dividing data into initial and candidate sample points, estimating parameter changes, calculating influence using a chain rule, and iteratively optimizing the model with a binary search algorithm to impute missing data, reducing training samples and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional imputation methods are used to impute missing medical diagnosis data, then data integrity is improved, but model complexity increases and processing efficiency decreases
Solution Approach 1:
The patent segments the training process into two phases: pre-training on complete data to establish basic imputation capability, and fine-tuning on data with missing values to adapt to specific missing patterns. This segmentation reduces overall model complexity while maintaining imputation effectiveness.
Solution Approach 2:
The patent applies preliminary action by pre-training the imputation model on complete medical diagnosis data before deploying it to handle missing data scenarios. This pre-training establishes a robust baseline model that can be quickly adapted to various missing data patterns without requiring complex retraining.
2Reliability
If traditional imputation methods are used to impute missing medical diagnosis data, then data integrity is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by using a lightweight imputation model that processes only the necessary features and dimensions required for effective imputation, rather than processing the entire dataset comprehensively. This reduces processing time while maintaining adequate imputation quality for clinical decision-making.
Solution Approach 2:
The patent changes parameters by optimizing the imputation model's architectural parameters and training parameters to achieve faster inference speeds. This includes adjusting model depth, width, and learning rates to balance imputation accuracy with processing efficiency for time-sensitive medical applications.
3Measurement precision
If more training samples are used to improve imputation accuracy, then imputation precision is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training the model on a large corpus of complete medical diagnosis data to establish strong feature representations and imputation patterns. This pre-training phase leverages abundant available data to build a robust foundation that requires less additional training time when adapting to specific missing data scenarios.
Solution Approach 2:
The patent extracts and utilizes transferable knowledge from pre-training on complete data, separating the general imputation capability acquisition from the specific missing pattern adaptation. This extraction allows the model to leverage insights from large datasets without requiring extensive retraining when deployed on smaller datasets with missing values.
Data Source
AI summary
The present disclosure discloses a missing medical diagnosis data imputation method and apparatus, an electronic device and a medium. The method includes the following steps: acquiring a medical diagnosis data set with data missing; randomly dividing original data into initial sample point data and candidate sample point data, and constructing and training a generative adversarial network initial imputation model by utilizing the initial sample point data; estimating an influence of sample points on a parameter of the generative adversarial network initial imputation model and a prediction result of the generative adversarial network initial imputation model by utilizing an influence function; and sampling a sample point with highest influence among the candidate sample point data by utilizing a binary search algorithm, and further iteratively optimizing the generative adversarial network initial imputation model so as to impute missing data for the medical diagnosis data.