Surgical Data Aggregation for Machine Learning Reliability
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Solution Overview
Problem
Incorporating non-traditional algorithms, such as machine learning, into medical technologies poses challenges due to the need for complete and regular surgical data, which existing systems often fail to provide effectively.
Innovation Solution
A computing system that aggregates and apportions available surgical data to create a more usable dataset for machine learning model analysis, generating substitute data to address incomplete or erroneous data sets, thereby enhancing the accuracy and reliability of machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning algorithms are incorporated into medical technology systems, then the capability for advanced data analysis and personalized patient care is improved, but the requirement for complete and regular surgical data creates challenges that worsen system reliability
Solution Approach 1:
The system performs preliminary actions by proactively identifying missing or problematic data points before machine learning analysis is executed. The surgical data aggregation module pre-processes data, identifies gaps, and generates substitute data in advance, ensuring data completeness is addressed before the ML model requires it, thus preventing reliability issues during actual analysis
Solution Approach 2:
The surgical data aggregation module acts as an intermediary between raw surgical data sources and machine learning algorithms. It receives incomplete or irregular data, processes it through substitution mechanisms using master surgical data, and outputs completed datasets that are suitable for ML analysis, thereby mediating the reliability gap between available data and ML requirements
2Reliability
If substitute data is generated to complete incomplete datasets, then the completeness of the dataset is improved, but the complexity of the data processing system increases
Solution Approach 1:
The system uses copying by creating substitute data entries that replicate the structure and format of existing master surgical data. When data points are missing, the system copies corresponding data from the master surgical dataset, ensuring the completed dataset maintains consistency with established data patterns without requiring complex generation algorithms
Solution Approach 2:
The system changes data parameters by transforming incomplete or irregular data into complete and regular data formats. The surgical data aggregation module adjusts data parameters such as completeness status, data type consistency, and format standardization, converting problematic data into ML-ready formats through parameter modification rather than complex reprocessing
Data Source
AI summary
Systems, methods, and instrumentalities are disclosed for aggregating and/or apportioning available surgical data into a more usable dataset for machine learning (ML) model (e.g., algorithm) interaction. A ML model may be more accurate and/or reliable if using complete and/or regular data. Aggregating and/or apportioning available surgical data may enable a more complete and/or regular dataset for ML model analysis.