Clinical Protocol Prediction System Using Hierarchical Transformers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Clinical trials are costly and prone to failures due to inaccuracies in protocol documents, which are not effectively assessed for likelihood of success or failure, leading to significant financial losses in the pharmaceutical industry.
Innovation Solution
A deep learning system leveraging hierarchical transformer networks processes textual information from protocol documents to predict the likelihood of successful execution, identifies risky sections, and provides recommendations for improvement based on historical data, using self-attention mechanisms and multiple machine learning models for natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If protocol documents are manually reviewed and assessed, then accuracy of evaluation may be improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical review processes with automated machine learning systems. Multiple ML models process different sections of protocol documents automatically, substituting human experts' time-consuming manual assessment with computational algorithms that provide predictions quickly while maintaining evaluation accuracy through ensemble methods and cross-validation.
Solution Approach 2:
The system creates computational copies of expert review processes through trained machine learning models. These models learn from historical protocol documents and outcomes, creating virtual replicas of expert assessment capabilities that can evaluate new protocols rapidly without requiring actual human experts for each review.
2Reliability
If comprehensive protocol assessment is performed to identify risks, then reliability of clinical trial prediction is improved, but system complexity increases
Solution Approach 1:
The patent divides the protocol document into multiple sections (e.g., eligibility criteria, intervention details, outcome measures) and assigns different specialized machine learning models to each section. This segmentation allows comprehensive assessment of all protocol aspects while managing system complexity through modular architecture, where each model focuses on specific content types.
Solution Approach 2:
The system employs a universal framework that integrates multiple machine learning models with different specialized functions. This multi-functional system handles various protocol sections, risk identification, and prediction tasks through a unified platform, reducing overall complexity compared to separate independent systems for each function.
3Measurement precision
If multiple machine learning models are used to process different sections, then prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
By segmenting the protocol into distinct sections and processing them in parallel with specialized models, the system achieves both high accuracy and efficiency. Each model processes its assigned section independently and simultaneously, reducing total processing time compared to sequential analysis while maintaining comprehensive evaluation through the ensemble of specialized models.
Data Source
AI summary
The methods and systems may improve the development of protocol documents used for clinical trials. The methods and systems may automatically estimate the likelihood of success or failure of executing a protocol document for a clinical study using a machine learning model that leverages several hundred thousand of past protocol documents and the outcomes of the clinical studies. The methods and systems may highlight sections of the protocol document that may increase a likelihood of an unsuccessful execution of the protocol document and may provide one or more recommendations to improve the highlighted sections of the protocol document.