Unified Clinical Data Model for Interactive Analysis
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Solution Overview
Problem
Clinical trial data is often stored in diverse formats, making it difficult to reconcile and analyze uniformly, leading to inefficiencies in monitoring safety and efficacy during trials, and transforming data to standardized formats like SDTM/ADaM is costly and time-consuming, limiting interactive analysis.
Innovation Solution
A unified general data model that transforms raw clinical trial data into a universal format using a hierarchical structure with dataset classes, enabling flexible representation and interactive analysis throughout the trial process, facilitated by machine learning algorithms for data classification and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinical trial data is transformed into standardized formats like SDTM/ADaM, then data compatibility and regulatory compliance are improved, but processing time and computational cost increase significantly
Solution Approach 1:
The system performs preliminary classification of data formats using machine learning algorithms before transformation. By identifying the source format and applying pre-defined mapping rules in advance, the system prepares transformation parameters beforehand, enabling faster execution during the actual transformation process without time-consuming on-the-fly decisions.
Solution Approach 2:
The patent introduces a unified data model as an intermediary layer between diverse source formats and the target standardized format. This intermediate representation serves as a common language that translates various input formats into a consistent internal structure, simplifying the transformation process and reducing computational complexity compared to direct one-to-one mappings.
2Adaptability or versatility
If a unified data model is implemented to standardize diverse data formats, then data consistency and analysis uniformity are improved, but system complexity increases
Solution Approach 1:
The unified data model is segmented into hierarchical classes (e.g., base class, domain-specific classes, and format-specific subclasses). This segmentation allows the system to handle diversity at different levels: the base class provides common functionality for all formats, while domain-specific classes manage particular data types, and format-specific classes handle unique source formats. This modular approach reduces overall system complexity by distributing complexity across manageable layers.
Solution Approach 2:
The unified data model implements a universal interface that can accept multiple input formats through a single standardized entry point. The system uses a common transformation pipeline that handles all formats uniformly, eliminating the need for separate processing logic for each source format. This multi-functionality approach reduces system complexity by consolidating transformation logic into a single reusable framework.
3Manufacturing precision
If data transformation is performed at the end of the trial, then data quality and completeness are improved, but availability for interactive analysis is delayed
Solution Approach 1:
The system performs data classification and transformation preparation in advance during data collection, rather than waiting for the trial to end. By continuously classifying incoming data and pre-transforming it into the unified model, the system ensures data is ready for interactive analysis from the start, eliminating the traditional end-of-trial transformation bottleneck.
Solution Approach 2:
The transformation process is made continuous rather than batched at the end of the trial. The system continuously transforms data as it arrives, maintaining an always-updated unified representation that is immediately available for analysis. This continuous transformation ensures both data quality through ongoing validation and immediate availability for interactive exploration.
Data Source
AI summary
Provided is a process, including: obtaining data assets characterizing a plurality of clinical studies, selecting a first subclass from a hierarchy of classes for the first data asset, selecting a second subclass from the hierarchy of classes for the second data asset, transforming the first data asset into the shared data format and data schema using the respective selected subclass storing transformation data including unique subject identifier mapping, the selected subclass, and the first format and first data schema of the first data asset, and saving resulting transformed first data asset in the shared data format and data schema in memory.