Solution
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TCS ADD™
TCS ADD™: A Suite of Modern and Open Technology Platforms
Industry
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Life Sciences
Patient-centered Care Models Taking Center Stage in Life Sciences
Highlights
- SDTM is a standardized format that enables exchange and submission of clinical trial data.
- To effectively automate the SDTM transformation process using AI and ML, a robust metadata repository framework is essential.
- ML algorithms are employed to automate the setup of transformation metadata from source variables to SDTM domains.
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SDTM transformation
At the different stages of a clinical trial, proper collection, management, and analysis of data are paramount. The Study Data Tabulation Model (SDTM) is quintessential for organizing and presenting clinical trial data.
As data complexities grow with the vast amounts of unstructured collected data and regulatory requirements evolve, the manual process of transforming source data into SDTM format becomes increasingly cumbersome.
However, a powerful combination of Artificial Intelligence (AI), Machine Learning (ML), and metadata repositories can revolutionize this process, making SDTM transformation more efficient, accurate, and adaptable than ever before.
This blog post explores the use of metadata repositories in SDTM transformation, metadata setup, and how AI and ML can automate metadata repository setup and foster SDTM transformation metadata setup for a study.
Robust MDR framework
To effectively automate the SDTM transformation process using AI and ML, a robust metadata repository (MDR) framework is key. This framework serves as a centralized hub that captures and manages metadata related to clinical studies, source data, SDTM domain metadata, SDTM mapping specifications, and transformation rules.
Moreover, it enhances collaboration and consistency among team members by providing a single source of truth for data transformations. It provides inheritability and scalability for future study creation. It maintains data integrity and traceability, streamlining the validation process, and expediting regulatory submission. The MDR ensures transparency and allows for efficient management of changes, ultimately leading to an accurate and compliant SDTM dataset.
MDR using AI and ML
Let us understand the crucial steps in leveraging AI and ML to create efficient MDRs
- Define data model: This includes identifying the necessary entities, attributes, and relationships to capture information relevant to SDTM transformation.
- Capture metadata: The next step is to capture metadata from various sources, such as data collection tools, and existing datasets. It includes information about variables, domains, mappings, transformation rules, and standards.
- Identification of source data: An AI algorithm helps in the correct identification of source data from different sources, where the key identifier may be different, but the source data point is the same e.g., a data point named “Date of Birth” and “Birth Date” essentially collect the same data and needs to be mapped to the same target variable. This mapping would be possible if we were able to identify the same data points on the backdrop of all existing data points maintained in the repository. Here, the data elements can be mapped accurately using Natural Language Processing (NLP) techniques that understand context and semantics.
- Develop transformation logic: SDTM domain variable derivation logic needs to be defined and implemented in the transformation metadata rules. These logics should address data derivation, and formatting to ensure the transformed data meets SDTM requirements.
In ML algorithms, historical transformation rules will be trained and applied automatically to new studies. These algorithms can identify patterns in the source data and generate transformation rules for variables with similar characteristics.
AI implementation
- Quality and reliability: Before implementing AI/ML models, invest time in thorough data pre-processing. Model performance and reliability are enhanced by clean, standardized data.
- Domain expertise: AI and ML algorithms thrive on large volumes of domain-specific data. Acquiring such data while adhering to patient privacy regulations is a significant hurdle.
- Interpretability: In regulated industries like pharmaceuticals, interpretability is crucial for compliance. Implement AI/ML models that are interpretable. Apply techniques like model visualization tools that provide reason and logic to enable the model’s accountability to arrive at decisions.
- Continuous learning and adaptation: Implement models that can be retrained periodically to accommodate changing regulatory standards. Use adaptive learning techniques to ensure models stay relevant over time.
Conclusion
Automating SDTM transformations using AI and ML through a robust metadata repository framework brings significant benefits to the field of clinical studies. By leveraging AI techniques such as NLP, pattern recognition, and data quality checks, organizations can streamline the SDTM transformation process, improve accuracy, and ensure regulatory compliance.
Acknowledgment
Charusheela Thakur, a Domain Consultant for TCS ADD™ Metadata Repository, contributed to this article. She has over two decades of experience in the pharma industry.
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