Clinical Trial Endpoint Aggregation via NLP Similarity Analysis
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Solution Overview
Problem
Analyzing clinical trial data is challenging due to the difficulty in aggregating and identifying relevant results for a specific disease indication, as existing systems lack efficient methods for handling large datasets and variations in search terms.
Innovation Solution
A computer-implemented method using a machine learning model to interpret and aggregate clinical trial results by matching endpoints to normalized options through similarity analysis, enabling the identification of relevant trials based on a specification, such as disease category or phase, and providing aggregated results in a structured format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If clinical trial data is aggregated from multiple external data sources, then the quantity and comprehensiveness of data increases, but the complexity of data processing and analysis increases
Solution Approach 1:
The system segments the complex data processing task into distinct functional modules: data collection from multiple external sources, data cleaning and preprocessing, endpoint extraction using NLP, similarity analysis, and result aggregation. Each module handles a specific aspect of the pipeline, making the overall system more manageable and maintainable despite processing large volumes of data from multiple sources.
Solution Approach 2:
The patent introduces intermediary components including a standardized data schema that mediates between diverse external data sources and the analysis engine, and NLP-based endpoint extraction that acts as an intermediary to normalize varying terminology. These intermediaries translate heterogeneous data into a unified format, reducing processing complexity while maintaining data comprehensiveness.
2Measurement precision
If search terms are standardized to exact matches, then data retrieval precision improves, but relevant results are missed due to terminology variations
Solution Approach 1:
The system changes the parameter of search matching from exact string matching to similarity-based matching using NLP techniques. By transforming the search parameter from literal term equality to semantic similarity, the system maintains precision while capturing variations in terminology such as synonyms, abbreviations, and different phrasings of the same clinical endpoint.
Solution Approach 2:
The patent creates normalized copies of endpoint terms through NLP processing, generating standardized representations of clinical endpoints while preserving the original meaning. This copying approach allows the system to match various terminology variations against a standardized reference set, ensuring comprehensive retrieval without sacrificing precision.
3Measurement precision
If manual analysis of clinical trial results is performed, then accuracy of interpretation is high, but processing time and resource requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis with automated NLP-based interpretation systems. Machine learning models trained on clinical trial data perform endpoint extraction, result interpretation, and similarity analysis automatically, maintaining high accuracy while reducing processing time from days or weeks to minutes or hours.
Solution Approach 2:
The system implements self-service capabilities where the NLP models automatically extract endpoints, interpret results, and generate aggregated findings without human intervention. The automated pipeline performs data cleaning, endpoint normalization, and analysis independently, freeing researchers from time-consuming manual tasks while maintaining consistent accuracy.
4Loss of information
If comprehensive search of all clinical trial data is conducted, then completeness of results is improved, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by using similarity thresholds to filter results - rather than analyzing every single clinical trial with equal depth, it performs comprehensive similarity analysis but focuses detailed processing on high-similarity matches. This approach maintains completeness for relevant results while reducing computational resources spent on clearly unrelated trials.
Solution Approach 2:
The patent applies local quality by varying the level of analysis based on relevance - high-similarity trials receive more thorough processing and validation, while lower-similarity trials receive streamlined processing. This differentiated approach ensures completeness for relevant findings while optimizing computational resource allocation across the entire dataset.
Data Source
AI summary
A computer-implemented method for clinical trial results endpoint-based analysis and dynamic aggregation, comprising the steps of receiving one or more selected clinical trials, wherein the one or more selected clinical trials match a specification; obtaining clinical trial results for the one or more selected clinical trials from at least one external data source; and interpreting, via a machine learning model, the obtained clinical trial results; importing the obtained clinical trial results as structured data. The method further comprising the steps of matching, based on a similarity analysis, via a processor, clinical trial endpoints identified in the obtained clinical trial results to corresponding normalized endpoint options; aggregating, based on the matched corresponding normalized endpoint options, the obtained clinical trial results to determine aggregated results; and providing the aggregated results.