Multimodal Hash Code Retrieval for Privacy-Preserving Treatment Planning
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Solution Overview
Problem
Health information technology systems for clinical decision making face limitations due to the availability of accurate and complete training data, which can be incomplete or inaccurate, and privacy concerns that restrict data sharing across institutions, affecting the efficacy of similarity matching in treatment planning.
Innovation Solution
A system that generates hash codes using data source-specific and superclass hash functions to train a retrieval model for matching similar cases without accessing raw patient-level data, allowing for personalized treatment planning across multiple data sources without data transfer, enabling faster searches with reduced memory consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training data is shared across institutions to improve similarity matching accuracy, then treatment planning accuracy is improved, but patient privacy is compromised
Solution Approach 1:
The patent introduces hash codes as an intermediary representation of patient data. Instead of sharing raw patient-level data across institutions, the system converts data into hash codes that preserve similarity information while preventing direct identification. The retrieval model operates on these hash codes rather than original data, acting as a mediator between privacy protection and similarity matching needs.
Solution Approach 2:
The system creates hash code copies of patient data that can be shared and processed without exposing the original sensitive information. These hash code representations capture essential features for similarity matching while being computationally efficient and privacy-preserving, allowing multiple institutions to use copies rather than original data.
2Measurement precision
If complete training data is used to improve model accuracy, then treatment suggestions are more accurate, but data storage and processing requirements increase
Solution Approach 1:
The patent extracts essential features from complete patient data and represents them as compact hash codes. Instead of storing and processing entire datasets, the system extracts key characteristics into condensed hash representations that retain the necessary information for similarity matching while dramatically reducing storage requirements.
Solution Approach 2:
The system transforms patient data from its original high-dimensional form into hash code representations with different parameters. This parameter transformation maintains the essential information needed for treatment planning while changing the data format to be more storage-efficient and computationally manageable.
3Measurement precision
If raw patient-level data is accessed for similarity matching, then matching accuracy is improved, but search time and computational resources increase
Solution Approach 1:
The patent uses hash codes as lightweight, computationally inexpensive representations that can be quickly processed and discarded. These hash code objects require minimal computational resources to manipulate compared to raw patient data, enabling faster search operations while maintaining matching accuracy.
Solution Approach 2:
The system performs preliminary conversion of patient data into hash codes before the actual similarity matching process. This preliminary action prepares the data in a format optimized for quick comparison and retrieval, reducing the computational burden during the actual search and matching operations.
Data Source
AI summary
A system, method, and computer program product for treatment planning are disclosed. The system includes at least one processing component, at least one memory component, a training module, a retrieval module, and a plan generator. The training module generates hash codes by hashing features from data sources with data source-specific hash functions, and generates superclass hash codes by hashing the generated hash codes with at least one superclass hash function. The retrieval module extracts features from case data, and locates features from the data sources that are similar to the extracted features. The plan generator calculates outcome probabilities for the case data based on known outcomes associated with the located features.