Contextual Patient Similarity Learning for Clinical Decision Support
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Solution Overview
Problem
Clinical decision support algorithms lack personalization and cannot account for every medical situation, relying on clinician experience for therapy decision-making, as they do not effectively identify contextual similarities between patients.
Innovation Solution
Techniques for learning and applying entity contextual similarities using artificial intelligence and statistical methods to generate template similarity functions, which compare feature vectors of patients, and a composite similarity function to identify contextually similar patients, facilitating intelligent treatment selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinical decision support algorithms use general guidelines for therapy decision-making, then they can provide standardized risk scores for patient deterioration, but they cannot account for every possible situation and lack personalization
Solution Approach 1:
The system pre-computes similarity scores between the query patient and all candidate patients in the database before treatment selection. These preliminary similarity calculations enable rapid retrieval of contextually similar patients during clinical decision-making, allowing the system to provide personalized recommendations without delaying treatment
Solution Approach 2:
The system creates virtual copies of real patients from the retrospective database, preserving their clinical characteristics and treatment outcomes. These copied patient records form the basis for identifying contextually similar patients, enabling the system to learn from historical data and apply it to current clinical decisions without exposing actual patient identities
2Reliability
If clinicians rely on past experience for therapy decision-making, then they can handle complex individual cases, but it increases the time required for decision-making and reduces efficiency
Solution Approach 1:
The system provides feedback to clinicians by presenting contextually similar patients and their treatment outcomes alongside the query patient case. This feedback mechanism allows clinicians to quickly assess what treatments worked well for similar patients without having to rely solely on their own experience, thereby improving decision quality while reducing time requirements
Solution Approach 2:
The system performs preliminary analysis by pre-calculating similarity scores and organizing patient data in advance. When a clinician needs to make a treatment decision, the system has already prepared the relevant similar patient cases, eliminating the need for time-consuming manual search and analysis during the critical decision-making moment
3Measurement precision
If the system compares all patient features for similarity, then it can achieve comprehensive matching, but it increases computational complexity and processing time
Solution Approach 1:
The system segments the patient feature space by dividing all patient features into multiple feature subsets, with each subset evaluated by a dedicated template similarity function. This segmentation allows the system to compare patients on specific clinical dimensions (e.g., demographics, vitals, labs) independently, improving measurement precision while reducing the computational burden of comparing all features simultaneously
Solution Approach 2:
The system employs multiple template similarity functions that each evaluate only a subset of patient features rather than requiring complete feature comparison. By using multiple partial evaluations with different weighting schemes, the system achieves comprehensive similarity assessment through aggregation of partial results, reducing overall computational complexity while maintaining accuracy
4Adaptability or versatility
If the system uses multiple template similarity functions with different weightings, then it can provide diversified views of patient similarity, but it increases the complexity of the similarity assessment model
Solution Approach 1:
The system segments the similarity assessment task by creating multiple template similarity functions, each focused on specific clinical contexts or feature subsets. Each template function provides a specialized perspective on patient similarity (e.g., one template for demographic similarity, another for clinical presentation similarity). This segmentation enables the system to handle diverse clinical scenarios without requiring a single overly complex model
Solution Approach 2:
The system merges the results from multiple template similarity functions into a composite similarity score using weighted aggregation. By combining the outputs of simpler template functions with appropriate weightings, the system achieves a comprehensive similarity assessment that captures diverse clinical perspectives without the complexity of designing a single monolithic similarity model
Data Source
AI summary
Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions (118) may be provided (602). Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function (120) may be provided (604) as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided (606) as context training data. An approximation function may be applied (608) to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained (610) based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.