Machine Learning Models for Clinical Trial Data Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data analysis technologies face challenges in processing and presenting clinical trial data from disparate sources, particularly in identifying anomalies and compliance risks associated with adverse events.
Innovation Solution
The use of machine-learning techniques to process and evaluate clinical trial data by applying trained learning models to identify anomalies and compliance risks, including the detection of adverse events and underreporting issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used to compile data from multiple disparate sources, then data aggregation can be achieved, but significant processing capabilities are required to generate indexes and longitudinal mappings, and data presentation becomes difficult
Solution Approach 1:
The patent replaces traditional mechanical data processing methods with machine learning models. Instead of manually generating indexes and mappings through complex processing systems, the invention uses trained ML models to automatically identify anomalies and patterns in clinical trial data, significantly reducing processing complexity while maintaining or improving efficiency
Solution Approach 2:
The system enables self-service data analysis by allowing the machine learning models to autonomously process and evaluate clinical trial data without requiring extensive manual intervention for index generation and data mapping. The models independently identify anomalies and generate insights from multi-source data
2Measurement precision
If machine learning models are applied to identify anomalies and compliance risks in clinical trial data, then identification accuracy improves, but model selection and training complexity increases
Solution Approach 1:
The patent segments the complex task of clinical trial data analysis into multiple specialized machine learning models, each trained to identify specific types of anomalies or compliance risks. This segmentation allows for higher precision in detecting particular patterns while managing overall system complexity through modular model deployment
Solution Approach 2:
The system changes parameters by selecting and applying different machine learning models based on the specific characteristics of the data and the type of anomaly being detected. This allows optimization of detection accuracy for different clinical scenarios without requiring a single overly complex model
3Reliability
If multiple machine learning models are used to evaluate different aspects of compliance risk, then detection comprehensiveness improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple specialized machine learning models during the development phase. These pre-trained models can then be rapidly deployed for production use, reducing processing time during actual compliance risk assessment while maintaining comprehensive detection capabilities across multiple risk dimensions
Data Source
AI summary
Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.