Graph-Based Code Recommendation for Scalable Predictive Analysis
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Solution Overview
Problem
Existing predictive data analysis frameworks face challenges in computational efficiency and scalability, particularly in fast-changing domains like the medical field, where new procedures are frequently added, leading to inefficiencies in code recommendation processes.
Innovation Solution
A graph-based code recommendation machine learning model is employed, utilizing predictive code nodes and inferred edge weight values updated based on observed co-occurrences within temporally-proximate subsets, enabling efficient and scalable predictive code recommendation through linear computational complexity and parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional predictive data analysis frameworks are used for code recommendation, then code recommendation functionality is provided, but computational efficiency deteriorates and scalability is limited
Solution Approach 1:
The system segments the code recommendation task into two distinct phases: an offline training phase where the graph-based model learns relationships from historical data, and an online inference phase where the trained model generates recommendations. This segmentation allows the complex computational workload to be distributed across different time periods, improving overall computational efficiency while maintaining system manageability.
Solution Approach 2:
The system performs preliminary training of the graph-based code recommendation model using historical code occurrence data before actual recommendation requests are processed. By pre-training the model in advance, the online recommendation process benefits from pre-computed relationships and patterns, significantly reducing the computational burden during real-time inference and improving productivity.
2Adaptability or versatility
If existing code recommendation systems are used, then code recommendations are generated, but scalability to fast-changing domains like medicine deteriorates
Solution Approach 1:
The system employs a dynamic graph-based model that can adapt to changing domains and codes. The graph structure allows for flexible addition of new codes and relationships without requiring complete system retraining. This dynamic capability enables the system to scale to fast-changing domains like medicine, where new procedures and codes are frequently introduced, while maintaining reasonable processing times through efficient graph traversal algorithms.
Solution Approach 2:
The system changes its operational parameters based on the domain and data characteristics. By adjusting training parameters, graph construction methods, and inference thresholds according to the specific domain requirements, the system can efficiently scale to diverse and evolving domains like medicine without excessive processing time penalties.
3Quantity of substance
If comprehensive code recommendation is performed, then more related codes are identified, but processing cycles increase
Solution Approach 1:
The system applies local quality by focusing the graph-based recommendation on the most relevant and strongly connected codes rather than uniformly processing all possible codes. By prioritizing edges and nodes with higher weights and stronger relationships in the graph, the system efficiently identifies the most pertinent related codes, improving the quantity of useful recommendations while minimizing unnecessary processing cycles.
Solution Approach 2:
The system performs partial action by generating and returning only the top-k most relevant code recommendations based on the graph model's confidence scores and edge weights, rather than exhaustively processing all possible relationships. This partial approach provides sufficient related codes for practical purposes while significantly reducing the processing cycles required compared to complete exhaustive search.
Data Source
AI summary
Solutions for more efficient and effective predictive code recommendation are disclosed. In one example, a method includes identifying a graph-based code recommendation machine learning model, wherein each inferred edge weight value of the graph-based code recommendation machine learning model is updated based at least in part on each compressed forward-adjusted temporal distance measure for an observed co-occurrence of any observed co-occurrences of a predictive code pair for the inferred edge weight value within one or more temporally-proximate occurrence subsets determined based at least in part on a plurality of training predictive code occurrences; processing the input predictive code using the graph-based code recommendation machine learning model to generate one or more related codes of the plurality of predictive codes for the input predictive code; and performing one or more prediction-based actions based at least in part on the one or more related codes.