Semi-Supervised Indexing for Patient Similarity
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Solution Overview
Problem
Existing approaches for patient similarity evaluation in medical research are inefficient in handling massive amounts of data, particularly in analyzing electronic medical records, which hinders effective clinical affinity and diagnostic proximity assessments.
Innovation Solution
A method is developed to index data using an objective function that combines data property and supervision components, optimized to partition nodes into child nodes, leveraging both expert knowledge and data characteristics to create a robust and accurate indexing tree without significantly increasing search time or tree construction time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data analytic tools are used to analyze electronic medical records, then the system structure is simple, but the efficiency in handling massive amounts of data is low
Solution Approach 1:
The patent segments the massive patient dataset into multiple partitions using a tree-based indexing structure. Each node in the tree represents a partition of the data space, allowing efficient localized processing. The objective function is applied recursively to split nodes into child nodes, creating a hierarchical segmentation that improves data processing efficiency while managing complexity through structured organization.
Solution Approach 2:
The patent transforms the data organization from a flat structure to a multi-dimensional tree structure. By introducing hierarchical levels (root node, intermediate nodes, leaf nodes) and spatial dimensions (data space partitions), the system efficiently handles massive data through dimensional organization, enabling faster retrieval and analysis without linearly increasing system complexity.
2Measurement precision
If patient similarity evaluation is performed on massive datasets, then the clinical affinity assessment accuracy is improved, but the computation time increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-processing and indexing the patient data before similarity evaluation. The objective function is used to pre-organize data into a tree structure with supervised and unsupervised components, creating an optimized data layout that enables fast retrieval during actual similarity assessments, thereby reducing computation time without sacrificing accuracy.
Solution Approach 2:
The patent replaces brute-force mechanical comparison methods with an optimized indexing system based on objective function optimization. Instead of comparing every patient pair directly, the system uses the tree structure and pre-computed data property components to substitute inefficient mechanical operations with faster indexed queries, maintaining measurement precision while reducing time loss.
3Reliability
If a supervised learning approach is used for data grouping, then the node purity is improved, but the requirement for labeled data increases
Solution Approach 1:
The patent merges supervised and unsupervised learning approaches into a unified objective function. The supervised component uses labeled data to improve node purity where available, while the unsupervised component handles the majority of unlabeled data through data property optimization. This combination achieves high reliability in node purity without requiring all data to be labeled, efficiently balancing the trade-off between purity and data requirements.
Data Source
AI summary
Systems and methods for indexing data include formulating an objective function to index a dataset, a portion of the dataset including supervision information. A data property component of the objective function is determined, which utilizes a property of the dataset to group data of the dataset. A supervised component of the objective function is determined, which utilizes the supervision information to group data of the dataset. The objective function is optimized using a processor based upon the data property component and the supervised component to partition a node into a plurality of child nodes.