Machine Learning Diagnosis Models Data Integration
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Solution Overview
Problem
Current diagnostic methods often fail to accurately detect undiagnosed diseases due to the lack of integration and analysis of disparate data sources, such as laboratory and medical records, which limits the recognition of patterns and probabilities for diagnosis.
Innovation Solution
The use of machine learning models trained on large, complex datasets combining data from various sources to generate probabilities for diagnosis, with data standardization and normalization processes to format input data into coherent structures for analysis, and feedback mechanisms to improve model accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used, then the diagnostic process is simple, but the diagnostic accuracy is insufficient due to lack of integration of disparate data sources
Solution Approach 1:
The patent combines multiple disparate data sources including laboratory data, medical record data, and geographical data into a unified analytical system. This merging of previously separate data streams enables comprehensive pattern recognition and significantly improves diagnostic accuracy by considering all relevant information together rather than in isolation.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that process and integrate disparate data sources. These models act as mediators between raw data from multiple sources and diagnostic conclusions, automatically identifying patterns and relationships that would be difficult for traditional methods to detect across heterogeneous data types.
2Measurement precision
If machine learning models are implemented, then diagnostic accuracy improves, but computational resource requirements increase
Solution Approach 1:
The patent performs data standardization and normalization as preliminary actions before feeding data into machine learning models. By pre-processing and structuring data in advance, the system reduces the computational burden during model execution, enabling accurate diagnostics while managing energy consumption through efficient data preparation rather than repeated complex processing.
3Measurement precision
If comprehensive data integration is performed, then pattern recognition capability improves, but data processing time increases
Solution Approach 1:
The patent applies data standardization and normalization as preliminary processing steps to transform disparate data into coherent structures before analysis. This pre-processing organizes data in advance, enabling faster and more accurate pattern recognition during the actual diagnostic process by reducing the complexity of real-time data integration.
Data Source
AI summary
The present disclosure describes methods and systems for machine learning models utilized for diagnosis suspecting. These methods and systems utilize machine learning models may be trained to diagnose diseases or conditions. The models may be trained with data from disparate sources that are aggregated and formatted to be utilized in these models.