Automated Clinical Trial Data Extraction from Structured and Unstructured Data Sources

Clinical trials represent a cornerstone of medical research, serving as the primary mechanism for evaluating the safety and efficacy of advanced therapeutic areas. These interventions encompass a broad spectrum, including pharmaceuticals, biological products, surgical and radiological procedures, medical devices, and behavioral treatments. The data generated from these trials, spanning from Phase I to Phase IV, is instrumental in obtaining regulatory approvals, guiding healthcare decisions, and expanding the other frontiers of medical knowledge. This information is captured in various forms, ranging from structured data fields in clinical trial registries to extensive unstructured text within study protocols, investigator reports, and published articles. The sheer volume and intricate nature of this data necessitate the adoption of automated methodologies to facilitate efficient analysis and knowledge discovery. Within lab-published clinical drug trial data, a significant challenge lies in the vast amounts of unstructured data generated from various sources. Challenges like data silos, data quality concerns, limited accessibility, lack of standardization, and complex preprocessing needs make analysis difficult, impacting drug discovery and clinical trials. Additionally, failure to manage unstructured data effectively can lead to regulatory non-compliance, penalties, and reduced data usability for decision-making.  

Addressing this kind of data is notoriously difficult and costly, often requiring manual review and interpretation, which is both time-consuming and prone to human error. To overcome these hurdles, Natural Language Processing (NLP) techniques are increasingly being leveraged to extract meaningful insights and patterns from this textual information. In this article we will see how MathCo brings its deep expertise in NLP to tackle this complicated problem enabling more efficient data analysis and ultimately accelerating the drug development process.  

Data Pre-processing for NLP Analysis

Imagine a vast collection of research papers, clinical trial reports, and patient notes related to a disease that is rich with information about potential treatments, disease progression, genetic factors, cognitive symptoms, and the impact of various interventions. However, this information is often locked within dense paragraphs of text. 

Without defining medical entities and their relationships, researchers and clinicians would face significant hurdles in trying to answer crucial questions to: 

• Which drugs have shown promise in slowing cognitive decline in the patients? 
• What are the specific biomarkers that are being targeted by different therapies? 
• Are there any genetic predispositions that correlate with the effectiveness of certain treatments? 
• What are the common side effects associated with the new medications? 
• How do different interventions impact specific cognitive functions (e.g., memory, language, executive function)? 

NLP techniques play a crucial role in making medical data more accessible by identifying and categorizing key entities within unstructured text through Named Entity Recognition (NER). These entities include diseases, drugs, symptoms, biomarkers, genetic markers, brain regions, cognitive assessment tools, and adverse events. For example, symptoms such as memory loss, cognitive decline, confusion, and behavioral changes can be extracted alongside biomarkers like protein accumulations, neuroimaging markers, and cerebrospinal fluid levels.  

Similarly, genes, brain regions, and assessment tools used to measure cognitive function can be identified. Once the entities are recognized, Relation Extraction (RE) techniques help establish meaningful relationships between them, such as identifying which treatments target specific biomarkers, which genetic factors influence disease risk, which symptoms are associated with disease progression, and which adverse events are linked to particular interventions. NLP models can extract structured relationships, such as a treatment affecting a biomarker, a cognitive function being measured by a specific tool, or a genetic variant being a risk factor for a condition. By structuring this information, NLP enables researchers and clinicians to efficiently analyze large volumes of medical data, uncover trends, and make more informed decisions by structuring the data as follows: 

• Knowledge Graphs: Where disease, drugs, symptoms, biomarkers, genes, etc., are represented as nodes, and the relationships between them (e.g., “treats”, “targets”, “is a hallmark of”) are represented as edges connecting these nodes.
• Relational Databases: With tables for entities and relationships, allowing for structured querying and analysis. 

Applying these NLP techniques makes unstructured data of the disease significantly more accessible for researchers and clinicians that helps them with:  

• Perform complex queries: “Show me all drugs that target amyloid-beta plaques and have shown a statistically significant slowing of cognitive decline in clinical trials.”
• Identify potential drug repurposing candidates: “Are there any drugs approved for other conditions that also show an impact on the disease-related biomarkers?”
• Understand disease mechanisms: Explore the relationships between specific genes, biomarkers, and cognitive symptoms.
• Compare the efficacy and safety profiles of different treatments: Analyze the relationships between drugs and their associated adverse events and cognitive outcomes.
• Build predictive models: Potentially use the structured data to predict disease progression or treatment response based on patient characteristics and biomarker profiles.

NLP Technique and Model Identification Strategies

Automated Drug Trial Data Extraction via Gen AI Framework: A Case Study

• Problem Statement: A leading pharmaceutical firm struggled with significant challenges in extracting crucial clinical trial data. Their existing manual processes were inefficient, resource-intensive, and prone to inaccuracies due to the complexity and diverse formats of the documents, inconsistent terminologies, and scalability limitations across multiple trials. This necessitated an intelligent, scalable, and automated data extraction framework. 
• Implementation: MathCo developed an AI-powered data extraction framework with three key modules. 
  • Module 1: Scalable Data Processing: This module established a robust data pipeline for ingesting various formats (PDFs, PPTs, images). It implemented structured taxonomy and version control for data traceability and enabled real-time extraction and validation to enhance accuracy from the outset. 
  • Module 2: AI-Powered Extraction Engine: This core module leveraged LLMs for highly accurate identification and extraction of key trial metrics. It integrated multi-modal AI to process text, tables, and images and applied contextual NLP techniques to standardize inconsistent terminology and abbreviations. 
  • Module 3: Interactive UI and Reporting: A web-based dashboard was developed for real-time data validation and visualization of insights. The module enabled downloading structured data (Excel, CSV) for further analysis and implemented automated alerts and tracking for missing or inconsistent values.
• Result and Impact: The implemented AI solution led to an 80% reduction in manual effort and a 3X faster data processing time, significantly improving operational efficiency and reducing costs, enhancing data accuracy, enabled scalability across drug trials, ensured regulatory compliance.  

Read the full case study here.