Ensemble Model Integrating Clinical and Para-Clinical Data for Diagnosis
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Solution Overview
Problem
Conventional medical diagnosis methods are slow and prone to human errors due to the reliance on single types of diagnostic information, such as medical images or biomedical signals, which may not provide sufficient precision in determining a subject's health status.
Innovation Solution
A machine learning-based method that integrates clinical data, medical images, biomedical signals, and para-clinical test results using ensemble models and classifiers to detect a comprehensive health status, facilitating more accurate disease diagnosis and treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic procedures use a single type of diagnostic information (medical images or biomedical signals), then the diagnostic process is simpler and faster, but the diagnostic precision and reliability are insufficient
Solution Approach 1:
The patent combines multiple types of diagnostic information (medical images, biomedical signals, and clinical test results) into a unified diagnostic system. The ensemble learning framework integrates predictions from multiple classifiers, each processing different data types, to produce a final diagnostic conclusion. This merging of diverse data sources directly addresses the insufficient precision of single-type diagnostic methods while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The diagnostic system functions as a composite information processing system, where different data types (images, signals, test results) serve as different 'materials' that complement each other. Each data type contributes unique diagnostic value, similar to how composite materials combine different substances to achieve superior properties. The ensemble model acts as the composite structure that integrates these diverse information sources to achieve higher diagnostic precision than any single data type alone.
2Reliability
If multiple specialists interpret different pieces of diagnostic information separately, then each specialist can focus on their expertise, but the overall diagnostic process becomes slow and time-consuming
Solution Approach 1:
The system performs preliminary processing of different diagnostic data types through specialized classifiers before final integration. Each classifier pre-processes its specific data type (images, signals, or test results) independently, preparing predictions that are then combined by the ensemble model. This preliminary action allows parallel processing of multiple data streams, significantly reducing the time required compared to sequential specialist review while maintaining comprehensive analysis for reliable diagnosis.
Solution Approach 2:
The diagnostic system segments the overall diagnostic task into independent modules, each handling a specific data type. The segmentation allows each classifier to focus on its specialized data type without interference from other modalities, improving processing efficiency. The ensemble model then integrates these segmented results, achieving both specialized analysis and comprehensive diagnosis simultaneously, thereby reducing total diagnostic time while maintaining high reliability.
3Reliability
If conventional methods rely on human interpretation of diagnostic information, then flexibility and adaptability are maintained, but human errors increase diagnostic risk
Solution Approach 1:
The ensemble learning framework acts as an intermediary between raw diagnostic data and final diagnostic conclusions. Instead of direct human interpretation, the system uses multiple classifiers and an ensemble model as intermediate processing layers that objectively analyze the data. This intermediary mechanism reduces human error while maintaining diagnostic accuracy, and the modular structure keeps system complexity manageable through standardized integration protocols.
Data Source
AI summary
A method for machine learning-based disease diagnosis and treatment of a subject. The method includes obtaining a clinical data set, obtaining a para-clinical data set, detecting a first status by applying a first classifier to the clinical data set, detecting a second status by applying a second classifier to the para-clinical data set, detecting a final status by applying a first ensemble model to the first status and the second status, and determining a treatment plan of the subject based on the final status. The clinical data set is associated with clinical symptoms of the subject. The para-clinical data set includes at least one of a plurality of medical images, a plurality of biomedical signals, and a plurality of para-clinical test results of the subject. Each of the first status, the second status, and the final status representing one of illness or healthiness of the subject.