NLP Models for Regulatory Question Classification
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Solution Overview
Problem
The regulatory approval process is hindered by the time-consuming and error-prone manual review of numerous questions in health assessment questionnaires and response to questions documents, where reviewers struggle to identify relevant questions and provide accurate answers due to lengthy inquiries and implicit questions.
Innovation Solution
The implementation of natural language processing (NLP) and deep learning models for classifying, summarizing, and generating responses to regulatory questions, utilizing techniques such as contextual embeddings and bidirectional text reading to automate the classification, identification of similar questions, and generation of answers, thereby streamlining the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of regulatory questions is performed, then reviewers can understand and answer questions, but the process becomes extremely time-consuming and delays regulatory approval
Solution Approach 1:
The system performs preliminary classification of regulatory questions using NLP models before human reviewers engage. The classification unit automatically categorizes questions into domains (e.g., clinical, safety, manufacturing) and identifies relevant documents, preparing the groundwork so that reviewers only need to focus on providing answers rather than scanning entire questionnaires
Solution Approach 2:
An NLP-based intermediary system is introduced between the regulatory questions and human reviewers. This intermediary automatically classifies questions, retrieves relevant documents, and presents curated information to reviewers, acting as a mediator that filters and organizes information before human intervention
2Measurement precision
If reviewers manually scan through all questions to determine relevance, then they can identify questions within their expertise, but this initial stage creates significant delay
Solution Approach 1:
The regulatory questions themselves are equipped with automatic classification capabilities through NLP models. The system enables questions to self-categorize into relevant domains and self-identify associated documents without requiring reviewers to manually assess each question's relevance to their expertise
3Loss of information
If lengthy regulatory inquiries are presented to users, then complete information is provided, but users take additional time to fully understand what is being sought
Solution Approach 1:
The NLP system extracts and separates the essential meaning and key information from lengthy regulatory inquiries. By pulling out the core question and relevant context while removing redundant wording, the system presents condensed versions that retain complete information while reducing comprehension time
4Adaptability or versatility
If manual review processes are used, then human judgment can be applied, but the process becomes error-prone with users skipping or misunderstanding questions
Solution Approach 1:
The system implements feedback mechanisms where classification results are continuously refined based on reviewer interactions and outcomes. NLP models learn from correct and incorrect classifications, adjusting their algorithms to improve accuracy over time while maintaining human oversight for complex judgments
Data Source
AI summary
In systems and methods for processing regulatory questions, textual data representing regulatory questions is obtained by one or more processors. The systems and methods also use one or more natural language processing models to classify the regulatory questions, generate answers to the regulatory questions, generate summaries of the regulatory questions, and/or identify documents that are similar to the regulatory questions. The systems and methods also store, transmit, and/or display data indicative of the classifications, answers, summaries, and/or similar documents.