NCI Data Jamboree (Project Abstract Submission): Submission #48

Presenter Information
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First Name: Anthony
Middle Initial: {Empty}
Last Name: Cristillo 
Degree(s): Ph.D., MBA
Position/Title/Career Status: Senior Vice President and Federal Health Lead 
Organization: Revolutional, LLC 
Organization Address:
McLean

Email: anthony.cristillo@revolutional.com

Additional Authors
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List of Additional Authors:
- First Name: Diana 
  Last Name: Castiblanco
  Post-nominal letters: Ph.D., FMACP, FNC
  Affiliation: Revolutional, LLC
- First Name: Rod 
  Last Name: Fontecilla
  Post-nominal letters: Ph.D. 
  Affiliation: Revolutional, LLC 
- First Name: Naga
  Last Name: Nandivelugu
  Post-nominal letters: B.E., MBA
  Affiliation: Revolutional, LLC 


Abstract Information
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Abstract Category: Enhancing data interoperability (e.g., data harmonization, data federation)
Abstract Keywords: Data interoperability, Data harmonization, AI readiness, Business rule extraction, Semantic data model
Abstract Title: AI-Assisted Business Rule Extraction To Improve Interoperability across Oncology Datasets, and Enable Accurate, Reproducible, and Trustworthy Analyses
Abstract:
Background: Interpretation of oncology treatment, response, and safety data, including line of therapy, adverse events, treatment response, disease progression, censoring, and evidence status, depends on business rules embedded within study protocols, data dictionaries, case report forms, and statistical analysis plans rather than explicit, computable data elements. These implicit rules limit interoperability across datasets and force AI systems to infer clinical meaning that was never formally represented, reducing reproducibility and trustworthiness. The value of explicit rule representation has precedent in oncology; for example, iRECIST formalized immune-related response criteria beyond RECIST, improving consistency in clinical trial interpretation.

Objective: Evaluate whether AI-assisted extraction and explicit representation of oncology business rules improve cross-source interpretability, mapping accuracy, and reproducibility of downstream analyses.

Methods: Using datasets from the NCI Clinical and Translational Data Commons (CTDC), we will leverage our FedRAMP/NIST800-53 AI-powered provisional patented platform (RISE) to identify business rules from heterogeneous clinical and research artifacts prior to data ingestion and integration. Extracted rules will be mapped to a lightweight canonical model consisting of nine core entities (e.g., Patient, Diagnosis, Treatment Episode, Exposure, Adverse Event, Response Assessment, Disease Progression, Outcome, and Evidence Source) and nine categories of governing business rules. Each rule will retain provenance, source version, extraction confidence, and expert review status. Cancer domain experts will validate the extracted rules to ensure factual grounding and clinical accuracy.

Expected Outcomes: This project does not seek to replace existing oncology standards or create a universal cancer data model. Instead, it evaluates whether explicit, computable representation of business rules provides a semantic layer that complements existing standards, improves interoperability across oncology datasets, and enables more accurate, reproducible, and trustworthy AI-enabled analyses. These findings will inform scalable approaches for semantic harmonization within the cancer research ecosystem.