Disease Probability Vector Matching for Patient Case Retrieval
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Solution Overview
Problem
Current case matching methods in medical databases are inefficient due to the time and resource-intensive process of finding similar patient cases, especially when dealing with patients who have overlapping medical conditions, and they often require manual diagnosis before classification, leading to spurious correlations and slow database searches.
Innovation Solution
A method that uses clinical profiles with multiple levels of specifications and a degree of membership concept, based on fuzzy set theory, to match current patient cases against stored profiles, enabling quick retrieval of similar cases by determining the degree of match across various data variables, including demographic, clinical, and treatment history, and updating matches over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large medical database is used to increase the chances of finding similar patients, then the quality and relevance of matched cases improves, but the time and computational resources required for searching increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing disease probability vectors for all patients in the database before actual case matching is needed. These vectors are calculated in advance based on patient data and stored for rapid retrieval, eliminating the need to perform complex computations during the matching process itself.
Solution Approach 2:
The patent creates simplified copies of patient data in the form of disease probability vectors that capture the essential characteristics needed for matching. Instead of comparing complete patient records, the system uses these compressed vector representations, which are much faster to process while preserving the critical information needed for finding similar cases.
2Measurement precision
If traditional case matching methods are used to find similar patients, then comprehensive comparison is achieved, but the process becomes time and resource intensive
Solution Approach 1:
The patent transforms patient data from traditional categorical formats into disease probability vectors with continuous probability values. This parameter transformation enables the use of efficient vector similarity calculations instead of traditional record-by-record comparison methods, significantly improving search efficiency while maintaining matching accuracy.
Solution Approach 2:
The patent replaces the mechanical process of comparing patient records with automated vector-based similarity calculations. Instead of manually or systematically comparing individual data fields between patients, the system uses mathematical operations on probability vectors to rapidly identify similar cases, substituting computational mathematics for traditional comparison mechanics.
3Measurement precision
If manual diagnosis is performed before case classification, then accurate disease identification is achieved, but the process becomes slow and resource intensive
Solution Approach 1:
The patent enables the system to perform diagnosis and classification automatically without requiring manual medical expertise. The disease probability vectors are computed algorithmically from patient data, allowing the system to self-diagnose and self-classify cases, eliminating the need for manual diagnosis while maintaining accuracy through mathematical probability calculations.
4Reliability
If overlapping medical conditions are considered in case matching, then more comprehensive patient similarity is achieved, but the complexity of performing matching increases significantly
Solution Approach 1:
The patent merges multiple disease probability vectors into a unified representation that captures overlapping medical conditions. Instead of treating each disease separately and requiring complex multi-criteria matching, the system combines probability information across multiple conditions into integrated vectors, simplifying the matching process while comprehensively accounting for overlapping conditions.
Data Source
AI summary
A method of retrieving similar patient cases from a medical database includes matching a current patient case against a plurality of clinical profiles resulting in a set of matching clinical profiles. The method further includes determining a degree of membership of the current patient case in each clinical profile from the set of matching clinical profiles based upon a degree of match between the current patient case and the clinical profile. For at least one clinical profile from the set of matching clinical profiles, the method includes retrieving those similar patient cases from the medical database that have a substantially corresponding degree of membership as the current patient case.