Machine Learning Diagnostic Test Planning Framework
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic test development processes are costly and time-consuming, as they often require administering multiple tests for various medical conditions, making it impractical to cover all possible diseases or conditions effectively.
Innovation Solution
A framework that uses machine learning techniques to analyze retrospective data of patients and diagnostic tests to cluster patients and tests, training classifiers to identify optimal diagnostic test plans that minimize the number of required tests while maximizing diagnostic accuracy, including sequence chains and parallel combinations of monolithic tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple diagnostic tests are administered to cover all potential medical conditions, then diagnostic accuracy is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent segments the comprehensive diagnostic testing process into targeted subsets of tests based on patient-specific risk factors, symptoms, and clinical presentations. Instead of administering all possible tests to every patient, the system divides the test universe into relevant subsets tailored to individual patient needs, thereby maintaining diagnostic accuracy while reducing time consumption.
Solution Approach 2:
The patent applies partial action by administering only the necessary subset of diagnostic tests required to achieve sufficient diagnostic accuracy for each patient, rather than performing all possible tests. The machine learning models determine the optimal partial set of tests that provides adequate diagnostic coverage without the excessive time and resource expenditure of comprehensive testing.
2Measurement precision
If multiple diagnostic tests are administered to cover all potential medical conditions, then diagnostic accuracy is improved, but cost increases significantly
Solution Approach 1:
The patent segments the diagnostic testing portfolio into cost-effective subsets tailored to patient risk profiles and clinical presentations. By dividing the comprehensive test menu into targeted groups based on probability of disease, symptom severity, and pre-test likelihood, the system maintains diagnostic accuracy while minimizing the quantity of expensive tests administered to each patient.
Solution Approach 2:
The patent changes the parameter of test selection from a static comprehensive approach to a dynamic risk-adjusted approach. Machine learning models continuously adjust which tests are recommended based on changing patient parameters such as symptoms, risk factors, and preliminary test results, optimizing the balance between diagnostic accuracy and cost effectiveness.
3Measurement precision
If comprehensive diagnostic testing is performed for all potential conditions, then diagnostic accuracy is improved, but device and process complexity increase
Solution Approach 1:
The patent creates a universal machine learning-based decision support system that handles multiple diagnostic scenarios, patient types, and test combinations through a single integrated platform. This multi-functional system replaces numerous specialized testing protocols with one adaptive engine that automatically tailors test recommendations to each patient's specific needs, reducing overall process complexity while maintaining comprehensive diagnostic coverage.
Data Source
AI summary
A framework diagnostic test planning is described herein. In accordance with one aspect, the framework receives data representing one or more sample patients, diagnostic tests administered to the one or more sample patients, diagnostic test results and confirmed medical conditions associated with the administered diagnostic tests. The framework trains one or more classifiers based on the data to identify diagnostic test plans from the diagnostic tests. The one or more classifiers may then be applied to current patient data to generate a diagnostic test plan for a given patient.