Symptom-Disease Bayesian Network Sampling Algorithm
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Solution Overview
Problem
Existing methods for learning Bayesian probabilities in bipartite networks, such as No-U-Turn Sampling (NUTS) and Markov Chain Monte Carlo (MCMC), face issues of adaptability and latency when modeling symptom-disease relationships from electronic health records.
Innovation Solution
A novel sampling algorithm that iteratively samples candidates from a matrix of probabilistic interactions among symptoms and diseases, dynamically adjusting the variance of random variables and using Kullback-Leibler (KL) divergence to evaluate and replace matrix members based on Bayesian probabilities, allowing for adaptive and efficient learning of symptom-disease relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If No-U-Turn Sampling (NUTS) or Markov Chain Monte Carlo (MCMC) methods are used to learn Bayesian probabilities, then the learning process is performed, but the adaptability is poor and latency is high
Solution Approach 1:
The patent changes the fundamental parameters of the sampling approach by using importance sampling with analytically tractable importance distributions instead of generic MCMC/NUTS methods. This allows the algorithm to adjust sampling parameters dynamically based on the specific structure of the bipartite Bayesian network, achieving both accuracy and speed. The importance distribution parameters are optimized to match the target posterior distribution while maintaining computational efficiency.
Solution Approach 2:
The patent performs preliminary analysis of the data structure and network topology before initiating the sampling process. By pre-computing sufficient statistics, identifying relevant symptom-disease pairs, and preparing importance distributions in advance, the algorithm avoids unnecessary computations during the actual sampling phase, thereby reducing latency and improving adaptability.
2Reliability
If traditional sampling methods are used, then Bayesian probabilities can be learned, but the computational latency is high and adaptability is poor
Solution Approach 1:
The patent replaces the mechanical iterative sampling process of traditional MCMC/NUTS methods with an importance sampling framework that uses analytically tractable importance distributions. This substitution eliminates the need for lengthy Markov chain iterations while maintaining sampling reliability. The importance distributions are designed to concentrate samples in high-probability regions, achieving reliable probability estimation with fewer iterations and reduced computational latency.
Solution Approach 2:
The patent introduces dynamic adaptation into the sampling process by adjusting importance distribution parameters based on observed data patterns and network structure. The algorithm dynamically identifies relevant symptom-disease relationships and adjusts sampling focus accordingly, improving both reliability and speed by concentrating computational resources on the most informative samples rather than uniformly sampling the entire parameter space.
Data Source
AI summary
A method and apparatus are provided that includes iteratively sampling candidates from medical records and evaluating whether ones of the candidates better explain a member from the medical records. The iterations replace the member with the candidates and depending on whether the candidates better explain the member from the medical records may be weighted in a next iteration.