Automated Clinical Trial Document Generation via Knowledge Graph
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The manual writing process of clinical trial documents is cumbersome, time-consuming, and skill-dependent, due to the need to search information from multiple sources and follow evolving regulatory guidelines, leading to low productivity and prolonged drug approval cycles.
Innovation Solution
A processor-implemented method and system for automated generation of clinical trial documents using natural language processing (NLP) techniques, a clinical trial knowledge meta model, and domain dictionaries to preprocess, extract knowledge, recommend relevant concepts and infotypes, and generate formatted documents based on standard templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual writing process is used for clinical trial documents, then document accuracy and regulatory compliance can be maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The patent replaces the manual mechanical writing process with an automated NLP-based system that uses natural language processing, knowledge graphs, and template engines to generate documents automatically, thereby reducing time consumption while maintaining accuracy through structured data validation
Solution Approach 2:
The patent introduces an intermediary knowledge graph representation layer that mediates between raw data sources and document templates, allowing automated information extraction and transformation while ensuring regulatory compliance through structured validation rules
2Reliability
If manual writing process is used for clinical trial documents, then regulatory compliance can be ensured, but productivity decreases
Solution Approach 1:
The patent performs preliminary actions by pre-processing data from multiple sources, extracting structured information, and populating knowledge graphs before document generation, enabling rapid automated document production that maintains regulatory compliance through pre-validates data structures
Solution Approach 2:
The patent substitutes manual compliance-checking processes with automated NLP validation systems that verify document content against regulatory guidelines using natural language processing and knowledge graph constraints, ensuring compliance while dramatically increasing productivity
3Loss of information
If information is searched from multiple sources manually, then comprehensive information can be gathered, but time and effort required increase
Solution Approach 1:
The patent merges multiple information sources (clinical trial protocols, regulatory guidelines, scientific literature) into a unified knowledge graph structure, enabling comprehensive information retrieval through a single integrated system rather than manually searching separate sources
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer that mediates between diverse information sources and the document generation process, organizing and indexing information from multiple sources to enable rapid automated retrieval and synthesis
4Extent of automation
If conventional text summarization techniques are used, then some automation can be achieved, but end-to-end holistic approach is lacking
Solution Approach 1:
The patent segments the complex document generation task into distinct modular components: data extraction, knowledge graph construction, information matching, template rendering, and validation, enabling automated end-to-end processing while managing complexity through modular architecture
Solution Approach 2:
The patent creates a universal automated system that handles multiple document types and regulatory guidelines through a single multi-functional NLP platform, achieving holistic end-to-end automation without requiring separate specialized systems for each function
Data Source
AI summary
The disclosure relates generally to methods and systems for automated generation of clinical trial documents. Conventional technologies for automated clinical trial documents writing lack an end-to-end, efficient, and a holistic approach on automating the overall clinical trial documents writing process. Methods and systems of the present disclosure employ a clinical trial knowledge model that contains concepts, infotypes and contexts, a configurable dynamic recommendation model, and a clinical trial template model for generating the clinical trial documents. The present disclosure enables the digitalization of information from different sources of information using meta-model based approach. For a given clinical trial use case, the method of the present disclosure recommends the applicable concepts and infotypes. The recommendation provides a guided search of information, reduces search complexity, and finally generates formatted clinical trial documents.