Clinical Data Integration System for Structured and Unstructured Analysis
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Solution Overview
Problem
Current computer systems for processing clinical trial data are limited in their ability to integrate and analyze both structured and unstructured data from various sources, which hinders the prediction of clinical trial outcomes and optimization of trial designs.
Innovation Solution
A software-based system that processes clinical trial data from multiple sources, integrates both structured and unstructured data, and uses AI-based models to predict clinical trial outcomes, providing insights for improving trial designs and increasing the likelihood of regulatory approval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional computer systems are used to view clinical trial data, then data access is provided, but the ability to integrate and analyze both structured and unstructured data from various sources is limited
Solution Approach 1:
The system segments data into structured and unstructured categories, processing each type through specialized pipelines. Structured data from clinical trials is parsed into standardized fields, while unstructured data from literature and reports undergoes separate NLP processing. This segmentation enables comprehensive data integration without overwhelming system complexity.
Solution Approach 2:
An AI-based model serves as an intermediary layer between diverse data sources and the analysis interface. This mediator automatically integrates structured clinical trial data with unstructured external data, performing normalization and feature extraction to bridge different data formats and sources seamlessly.
2Measurement precision
If AI-based models are used to predict clinical trial outcomes, then prediction accuracy is improved, but the amount of data processing and computational resources required increases
Solution Approach 1:
The system performs preliminary data processing by pre-processing and normalizing clinical trial data before AI model analysis. Structured data is cleaned and standardized in advance, and unstructured data undergoes preliminary NLP extraction to identify key features. This preliminary action reduces the computational burden during actual prediction, balancing accuracy with processing efficiency.
3Reliability
If comprehensive clinical trial data from multiple sources is integrated, then the quality of predictive analytics is improved, but the complexity of data management and processing increases
Solution Approach 1:
The system implements a universal data integration architecture that handles multiple data types through standardized processes. The same AI model framework processes both structured clinical trial data and unstructured external data, applying consistent normalization and analysis methods. This multi-functional approach improves predictive reliability while managing complexity through unified data management protocols.
Data Source
AI summary
A system is provided that is capable of processing clinical trial data from one or more sources, both structured and unstructured data, and integrate such data in a database for access by users. Further, such trial data is combined with other types of data to train an AI-based model, for the purpose of determining a probability of a particular trial outcome, among other insights. Further, the system can provide insights into optimizing this probability, for example by optimizing elements of the clinical trial design. Further, the system stores the acquired data from multiple sources and provides search, comparison, and reporting capability within a single interface.