Vector-Embedding Cohort Classification for Retrospective Patient Data
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Solution Overview
Problem
The challenge in accurately classifying a user's medical experiences by aligning them with historical data from past patients is compounded by the vast diversity of patient histories and subtleties of individual medical conditions, leading to inaccurate analyses that can affect treatment plans and patient outcomes.
Innovation Solution
An apparatus and method for classifying a user to a cohort of retrospective users using a processor to generate vector embeddings of user data, query inputs, and cohort data, enabling precise alignment and classification through a machine-learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification methods are used to align user medical data with historical patient data, then the process is simpler to implement, but the classification accuracy deteriorates due to the vast diversity of patient histories and subtleties of individual medical conditions
Solution Approach 1:
The patent replaces traditional mechanical classification systems with vector embedding-based semantic representation. User medical data and historical patient data are transformed into vector embeddings that capture semantic meaning, allowing for more accurate similarity matching despite the diversity and subtlety of medical conditions. This substitution enables the system to handle complex medical data patterns that traditional methods cannot capture.
Solution Approach 2:
The patent changes the parameter representation from discrete categorical labels to continuous vector embeddings. By representing medical data in a continuous vector space where semantic similarity is preserved, the system can achieve higher classification accuracy. The vector embeddings allow for nuanced comparisons of medical conditions, treatments, and outcomes, capturing subtleties that discrete parameters would miss.
2Measurement precision
If vector embedding generation is implemented to improve data alignment, then classification precision is improved, but computational resource consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing vector embeddings for historical patient data in a database. When a user's medical data needs to be classified, the system only needs to generate one vector embedding for the user and compare it against the pre-computed embeddings, rather than generating embeddings for all historical data in real-time. This significantly reduces computational energy consumption during query operations.
3Adaptability or versatility
If multiple cohorts are generated to improve classification thoroughness, then the comprehensiveness of analysis is improved, but the time required for processing increases
Solution Approach 1:
The patent introduces a new dimension of similarity measurement by using vector embeddings in a high-dimensional vector space. Instead of comparing patients based on discrete categorical attributes alone, the system compares the semantic meaning of medical data in a continuous multi-dimensional space. This allows for more nuanced and comprehensive cohort generation that captures subtle relationships between medical conditions, treatments, and outcomes without requiring exhaustive processing of all possible attribute combinations.
Data Source
AI summary
An apparatus and method for classifying a user to a cohort of retrospective users is disclosed. The apparatus includes at least a processor and a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive user data of a user, generate a vector embedding of the user data, generate a query input, generate a plurality of cohorts of retrospective users using cohort data extracted from a cohort database based on the query input, wherein generating the plurality of cohorts includes generating a set of vector embeddings of the cohort data, classify, based on the vector embedding and the set of vector embeddings, the user data to at least a cohort of the plurality of cohorts of the retrospective users, and output the at least a cohort through a user interface.